{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Weight Initialization\n",
    "In this lesson, you'll learn how to find good initial weights for a neural network. Having good initial weights can place the neural network close to the optimal solution. This allows the neural network to come to the best solution quicker. \n",
    "\n",
    "## Testing Weights\n",
    "### Dataset\n",
    "To see how different weights perform, we'll test on the same dataset and neural network. Let's go over the dataset and neural network.\n",
    "\n",
    "We'll be using the [MNIST dataset](https://en.wikipedia.org/wiki/MNIST_database) to demonstrate the different initial weights. As a reminder, the MNIST dataset contains images of handwritten numbers, 0-9, with normalized input (0.0 - 1.0).  Run the cell below to download and load the MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting MNIST Dataset...\n",
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Data Extracted.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "import helper\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "print('Getting MNIST Dataset...')\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "print('Data Extracted.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network\n",
    "<img style=\"float: left\" src=\"images/neural_network.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the neural network, we'll test on a 3 layer neural network with ReLU activations and an Adam optimizer.  The lessons you learn apply to other neural networks, including different activations and optimizers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save the shapes of weights for each layer\n",
    "layer_1_weight_shape = (mnist.train.images.shape[1], 256)\n",
    "layer_2_weight_shape = (256, 128)\n",
    "layer_3_weight_shape = (128, mnist.train.labels.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Weights\n",
    "Let's start looking at some initial weights.\n",
    "### All Zeros or Ones\n",
    "If you follow the principle of [Occam's razor](https://en.wikipedia.org/wiki/Occam's_razor), you might think setting all the weights to 0 or 1 would be the best solution.  This is not the case.\n",
    "\n",
    "With every weight the same, all the neurons at each layer are producing the same output.  This makes it hard to decide which weights to adjust.\n",
    "\n",
    "Let's compare the loss with all ones and all zero weights using `helper.compare_init_weights`.  This function will run two different initial weights on the neural network above for 2 epochs.  It will plot the loss for the first 100 batches and print out stats after the 2 epochs (~860 batches). We plot the first 100 batches to better judge which weights performed better at the start.\n",
    "\n",
    "Run the cell below to see the difference between weights of all zeros against all ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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58+enZGRkHHHf16xZ8381vG5lr6riwQcf3Pnoo4+eNo/60qVLV02ZMqXek08+2WzmzJkH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\n66qmpKjee69tmzBBFVBdvty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MWppVkc+ZYxl3eQuMBMLNsnNzgWeescC7YIH1+L7kktK/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\n8P9KAih8G8wOdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3014a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   11.260% -- All Zeros\n",
      "    9.900% -- All Ones\n",
      "Loss\n",
      "    2.300  -- All Zeros\n",
      "  372.644  -- All Ones\n"
     ]
    }
   ],
   "source": [
    "all_zero_weights = [\n",
    "    tf.Variable(tf.zeros(layer_1_weight_shape)),\n",
    "    tf.Variable(tf.zeros(layer_2_weight_shape)),\n",
    "    tf.Variable(tf.zeros(layer_3_weight_shape))\n",
    "]\n",
    "\n",
    "all_one_weights = [\n",
    "    tf.Variable(tf.ones(layer_1_weight_shape)),\n",
    "    tf.Variable(tf.ones(layer_2_weight_shape)),\n",
    "    tf.Variable(tf.ones(layer_3_weight_shape))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'All Zeros vs All Ones',\n",
    "    [\n",
    "        (all_zero_weights, 'All Zeros'),\n",
    "        (all_one_weights, 'All Ones')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the accuracy is close to guessing for both zeros and ones, around 10%.\n",
    "\n",
    "The neural network is having a hard time determining which weights need to be changed, since the neurons have the same output for each layer.  To avoid neurons with the same output, let's use unique weights.  We can also randomly select these weights to avoid being stuck in a local minimum for each run.\n",
    "\n",
    "A good solution for getting these random weights is to sample from a uniform distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform Distribution\n",
    "A [uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous%29) has the equal probability of picking any number from a set of numbers. We'll be picking from a continous distribution, so the chance of picking the same number is low. We'll use TensorFlow's `tf.random_uniform` function to pick random numbers from a uniform distribution.\n",
    "\n",
    ">#### [`tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)`](https://www.tensorflow.org/api_docs/python/tf/random_uniform)\n",
    ">Outputs random values from a uniform distribution.\n",
    "\n",
    ">The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded.\n",
    "\n",
    ">- **shape:** A 1-D integer Tensor or Python array. The shape of the output tensor.\n",
    "- **minval:** A 0-D Tensor or Python value of type dtype. The lower bound on the range of random values to generate. Defaults to 0.\n",
    "- **maxval:** A 0-D Tensor or Python value of type dtype. The upper bound on the range of random values to generate. Defaults to 1 if dtype is floating point.\n",
    "- **dtype:** The type of the output: float32, float64, int32, or int64.\n",
    "- **seed:** A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.\n",
    "- **name:** A name for the operation (optional).\n",
    "\n",
    "We can visualize the uniform distribution by using a histogram. Let's map the values from `tf.random_uniform([1000], -3, 3)` to a histogram using the `helper.hist_dist` function. This will be `1000` random float values from `-3` to `3`, excluding the value `3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1022d62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.hist_dist('Random Uniform (minval=-3, maxval=3)', tf.random_uniform([1000], -3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram used 500 buckets for the 1000 values.  Since the chance for any single bucket is the same, there should be around 2 values for each bucket. That's exactly what we see with the histogram.  Some buckets have more and some have less, but they trend around 2.\n",
    "\n",
    "Now that you understand the `tf.random_uniform` function, let's apply it to some initial weights.\n",
    "\n",
    "### Baseline\n",
    "\n",
    "\n",
    "Let's see how well the neural network trains using the default values for `tf.random_uniform`, where `minval=0.0` and `maxval=1.0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zro9zrk+LFoeV3xJDeNfYqlWQng5Nm5Z9fswxMH9+fOomIiISQ9UNQtfip86v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\nhq5d4b//2w+E3rPHn5eS4su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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170631d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   65.340% -- tf.random_uniform [0, 1)\n",
      "Loss\n",
      "   64.356  -- tf.random_uniform [0, 1)\n"
     ]
    }
   ],
   "source": [
    "# Default for tf.random_uniform is minval=0 and maxval=1\n",
    "basline_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Baseline',\n",
    "    [(basline_weights, 'tf.random_uniform [0, 1)')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss graph is showing the neural network is learning, which it didn't with all zeros or all ones. We're headed in the right direction.\n",
    "\n",
    "### General rule for setting weights\n",
    "The general rule for setting the weights in a neural network is to be close to zero without being too small. A good pracitce is to start your weights in the range of $[-y, y]$ where\n",
    "$y=1/\\sqrt{n}$ ($n$ is the number of inputs to a given neuron).\n",
    "\n",
    "Let's see if this holds true, let's first center our range over zero.  This will give us the range [-1, 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4gblz5xbtJvsFngWaP39+SPuPP/6YZxvZ4MGDSU5OZv369fTt25caNWpQt27d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\nv/+DGTNU/l1ERBqFgghSQHd8jafpeHhaiPckLQIIIVyDh57b8Gp96wH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LlzF//rRffuhd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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1222f6d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   73.840% -- tf.random_uniform [0, 1)\n",
      "   89.360% -- tf.random_uniform [-1, 1)\n",
      "Loss\n",
      "   13.700  -- tf.random_uniform [0, 1)\n",
      "    5.470  -- tf.random_uniform [-1, 1)\n"
     ]
    }
   ],
   "source": [
    "uniform_neg1to1_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -1, 1)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -1, 1)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -1, 1))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    '[0, 1) vs [-1, 1)',\n",
    "    [\n",
    "        (basline_weights, 'tf.random_uniform [0, 1)'),\n",
    "        (uniform_neg1to1_weights, 'tf.random_uniform [-1, 1)')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going in the right direction, the accuracy and loss is better with [-1, 1). We still want smaller weights. How far can we go before it's too small?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Too small\n",
    "Let's compare [-0.1, 0.1), [-0.01, 0.01), and [-0.001, 0.001) to see how small is too small.  We'll also set `plot_n_batches=None` to show all the batches in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UK0769Yf4/+T24CUak6iaAAUTmTsinEfVxRQe/yxgUS2P15P+uv4eYdkAvMRsvX881/v1\nj4uyzlon6n67Nngf/L7ESy5fA/pUc26URijviJewlOAlmn+NdC4n4+/Mr98dbzadnXjJz1+A3Aj1\nDgGvRSg/E1iM14u7Ce9DXZUEO9bz1K/7DvBELV+Hxf66/hhh2VB/H4v9c2Id3oWBjoiyzlon6rV8\nTdcBH8f7mvh1o753+PWqez8tDat3PzFegdivPx5visO9wAfAlTWcu+Gvc1O8JH0D3vvMu4Rd0TaO\n8zSp7z266aZbcm/mnH7cXRP/x1Xn4A0lKXXO7UjgugcB84BeruLCQolY71/xYo16xcxkM7M38Hr4\nLwEOuNiG4tQLM7sP70dwx7u6/fgqYczsKLx/8sOdcy+lOJbOeInTWLwk6+t0OU61kay/s/piZr3x\nroXQxzm3Mlr9JMfSH+8DxsV4H9a/cs4dSmVMyWZm/wLWOeeuTHUsyZRO7z0iUiHlY9TN7FdmtsS8\ny1hvNrMXzaxbWJ0Z5l3OPvQ2P6xOMzN7xMy2mneJ9DlW9ep88ToG7+vjxQlaX9AAYFaCk/R8vIuZ\n3JGodSbAmXjH7+lUBxKmPzA5zZLPccD7afaP8g94vePfSXUgcRpAgv/O6tl44H9TnaSHcHjjsb/E\nGxZ32DKzVnhXVp2Q6ljqQTq+94g0einvUfcT7ll4PUYZwG/xZhTp7pzb69eZgTcP8HVUzA++P7R3\n28ym4c3zfS3e8IxH8K5Sd3Yd48un4opwu5xzS2qqL5WZWR8qporckkbJhkTh/4A49FLiH7iwqz5K\n42LeBc9CZ/X5l0viTDwiIo1dyhP1cP4MEV/ijd17yy+bAbRxzl1aTZvWeD22VzrnXvTLTsSbYup0\nJdciIiIi0tCkfOhLBG3xvlrdHlY+wB8as8bMpoZNL1aI1xv/WrDAObcWb1aBM5IdsIiIiIhIomWk\nOoBQ/hzVDwFvhY0nfQVv3th1eHO8/hbv0tdnOO8rgaPwfqj4ddgqNxP5SnEiIiIiImktrRJ1vHm6\ne1B5XCzOudALOHxoZivx5kwegDcDQa35F6S5AG/e3n3xrENERKSRag58A/iHc25bolduZsfiXaxL\n5HC11Tn3ebRKaZOom9nDeLOVnO2cK66prnNunZltBY7HS9Q3AU3NrHVYr/qRVFzaPNwFpN8sJCIi\nIg3J9/AuGpUwZnZsIBBYW1ZW1jyR6xVJJ4FAYJ+ZnRgtWU+LRN1P0i8G+sfy6cLMjgba412sA7yL\n0JTiXbQn9Mekx+JdKCSSTwGeeuopunfvXpfw08Ytt9zCgw8+mOowEuJw2hfQ/qSzw2lfQPuTzg6n\nfVm9ejVXXXUV+P9LE6xDWVlZ88Pp/7NIKP/vpznet0bpnaib2VRgBN5VH3eb2ZH+oh3OuX1mlo13\nFbfn8XrHj8e7cttHwD8AnHNfm9njwBQzK8G7UtvvgbdrmPFlH0D37t0pKCiopkrD0qZNG+1LmtL+\npK/DaV9A+5PODqd9CZG0oaOH0/9nkXilPFEHbsCb5WVhWPlIYCbeZY57AdfgzQizES9BnxB2oZpb\n/LpzgGbA34Gbkhm4iIiIiEiypDxRd87VOEWkc24fcGEM69kP/Mi/iYiIiIg0aOk4j7qIiIiISKOn\nRP0wMmLEiFSHkDCH076A9iedHU77AtqfdHY47YuI1A8l6oeRw+mfwOG0L6D9SWeH076A9iedHU77\nIvEbOXIkgUCAQCBAr169Uh1OVKtXryYzM5NVq1ZFrywJp0RdREREpB7l5uby9NNPc88998RUf/bs\n2Vx99dV069aNQCDAwIED6xzDe++9x5gxY+jbty9NmzalSZMmEet1796dwYMHM2HChDpvU2pPibqI\niIhIPcrOzmbEiBEMGjQopvrTpk1j3rx5HHvsseTk5CQkhvnz5zN9+nQCgQDHHXdcjXVvuOEGXnzx\nRdatW5eQbUvslKiLiIiIpLGnnnqKHTt2sGDBAvLy8hKyzjFjxrBjxw6WLFnCeeedV2Pd8847j7Zt\n2/KXv/wlIduW2ClRFxEREUljnTp1Svg6c3NzadasWUx1MzIyGDBgAHPnzk14HFIzJeoiIiIiUqPC\nwkL+/e9/s2vXrlSH0qik/IJHIiIiInHbswfWrEnuNvLzoUWL5G4jzXXt2pWysjLWrFlD3759Ux1O\no6FEXURERBquNWugsDC52ygqgoKC5G4jzbVr1w6ArVu3pjiSxkWJuoiIiDRc+fleIp3sbSRZSUkJ\nBw4cKH+elZVF69atk77dWDnnADCzFEfSuChRFxERkYarRYvDorf70ksvZdGiRYCXDF977bVMnz49\nxVFVKCkpAaBDhw4pjqRxUaIuIiIikmJTpkwpT4YBOnbsmMJoqlq3bh2BQIBu3bqlOpRGRYm6iIiI\nSIr16dMn1SHUqKioiJNOOolWrVqlOpRGRYm6iIiISBpbvHgxb775Js45tmzZwp49e7jrrrsA6Nev\nH2effXZ53UAgwIABA3j99ddrXOfnn3/Ok08+CcDSpUsBytfZuXNnrrrqqvK6paWlLFq0iLFjxyZ0\nvyQ6JeoiIiIiaez1119n8uTJ5c+3bNnChAkTAJg4cWJ5or57924gtmEz69at44477qj049DgOvv3\n718pUV+wYAElJSVcc801dd8ZqRUl6iIiIiL1qKysjG3btpGRkUGbNm2i1p84cSITJ06MWm/RokUE\nAgFuvfXWqHX79+9PWVlZTPH+8Y9/ZOjQoXTt2jWm+pI4StRFRERE6tH69evJzc2lZ8+efPDBBwlb\n78KFCxkxYgQ9evRI2DrXrFnD/Pnzef/99xO2TomdEnURERGRejJ+/HiuvvpqAFq2bJnQdd93330J\nXR9Afn5+pfndpX4pURcRERGpJ/n5+eTXwwWU5PAQSHUAIiIiIiJSlRJ1EREREZE0pERdRERERCQN\nKVEXEREREUlDStRFRERERNKQEnURERERkTSkRF1EREREJA0pURcRERERSUNK1EVERETqyciRIwkE\nAgQCAXr16pXqcNLeo48+SufOnTl48GCqQ0kJJeoiIiIi9Sg3N5enn36ae+65J+Y28+bNo7CwkKys\nLDp37sykSZM4dOhQTG2nTZvG8OHD6dy5M4FAgO9///vxhl7JP//5T771rW+RnZ1NXl4e48aNY/fu\n3TG1nT17NldffTXdunUjEAgwcODAiPWuu+46Dhw4wKOPPpqQmBsaJeoiIiIi9Sg7O5sRI0YwaNCg\nmOq/8sorDB06lJycHB5++GGGDh3KnXfeyc033xxT+/vuu4833niDnj17kpmZWZfQy61YsYLzzjuP\nffv28eCDDzJq1Cgee+wxhg8fHlP7adOmMW/ePI499lhycnKqrdesWTOuvfZapkyZkpC4G5qMVAcg\nIiIiItX72c9+Ru/evfnHP/5BIOD1sbZq1Yrf/va3jBs3jm7dutXY/s033+SYY44pb5cIt956Kzk5\nOSxatIjs7GwAOnfuzOjRo1mwYAHnnXdeje2feuopOnXqBMDJJ59cY93hw4dz3333sXDhQgYMGJCQ\n+BsK9aiLiIiIpKnVq1ezevVqRo8eXZ6kA4wZM4aysjLmzJkTdR3BJD1Rdu7cyYIFC7j66qvLk3SA\na665huzsbGbPnh11HcEkPRYFBQXk5OQwd+7cuOJtyJSoi4iIiKSp5cuXY2YUFhZWKs/Ly+Poo49m\n+fLl9R7TypUrKS0trRJTZmYmvXv3TkpMBQUFvP322wlfb7pToi4iIiKSpoqLiwEvMQ+Xl5fHxo0b\n6zskiouLMbN6jalr166sWrUq4etNdxqjLiIiIg3Wnj2wZk1yt5GfDy1aJHcb1dm7dy/g/agyXPPm\nzdm5c2d9hxQ1puDyRGrXrh179+5l3759NG/ePOHrT1dK1EVERKTBWrMGwkZgJFxRERQUJHcbJSUl\nHDhwoPx5VlYWrVu3JisrC4D9+/dXabNv377y5fUpFTE55wAws4SvO50pURcREZEGKz/fS6STvY1k\nu/TSS1m0aBHgJaPXXnst06dPLx9eUlxcXOUHmMXFxZx22mnJDy5MXl4ezrnyYTnhMXXs2DHh2ywp\nKaFFixYRe/EPZ0rURUREpMFq0SL5vd31YcqUKZSUlJQ/Dya7vXv3xjnH0qVL6du3b/ny4uJivvji\nC2644YZ6j7Vnz55kZGSwdOlShg0bVl5+8OBBVqxYwRVXXJHwba5bt47u3bsnfL3pTj8mFREREUmx\nPn36MHDgwPJbvt+N36NHD/Lz83nsscfKh38ATJ06lUAgwGWXXVZetnfvXtauXcu2bduSGmvr1q05\n77zzeOqppypdiXTmzJns3r270kWPSktLWbt2LZs2barTNpctW8aZZ55Zp3U0ROpRFxEREUlj999/\nPxdffDHnn38+V155JStXruSRRx5h1KhRnHjiieX1lixZwjnnnMOkSZOYMGFCeflLL73E+++/j3OO\ngwcP8v7773PXXXcBMGTIkPILDn322Wd06dKF6667junTp9cY01133cVZZ51Fv379GD16NOvXr2fK\nlClccMEFnH/++eX1NmzYQPfu3ausc/Hixbz55ps459iyZQt79uwpj6lfv36cffbZ5XWLiorYvn07\nl1xySR2OYsOkRF1EREQkjQ0ePJgXXniBX//619x8883k5uZy++23c8cdd1Spa2ZVfnD5/PPPM3Pm\nzPLnK1asYMWKFYB3MaRgor5r1y6AmMaY9+nThwULFjB+/Hh+8pOf0KpVK0aNGsXdd98dU0yvv/46\nkydPLn++ZcuW8g8XEydOrJSo/+///i+dO3dudFclBSXqIiIiIvWqrKyMbdu2kZGRQZs2bWJqM2TI\nEIYMGVJjnf79+3Po0KEq5TNmzGDGjBlRt7Fo0SJatmzJuHHjYorpzDPPZPHixTXW6dy5c8SYJk6c\nyMSJE6Nu48CBA8ycOZNbb701ppgONxqjLiIiIlKP1q9fT25ubqVe43SwcOFCxo0bR25ubqpDKTdj\nxgyaNm3K9ddfn+pQUkI96iE/zBARERFJpvHjx3P11VcD0LJlyxRHU9ns2bNTHUIV119/faNN0kGJ\nOpSVpToCERERaSTy8/PLZ3QRiUZDXyKMmxIRERERSTUl6krURURERCQNKVFXoi4iIiIiaUiJuhJ1\nEREREUlDStSVqIuIiIhIGkp5om5mvzKzJWb2tZltNrMXzaxbhHqTzWyjme0xs1fN7Piw5c3M7BEz\n22pmO81sjpkdETUAJeoiIiIikoZSnqgDZwN/AE4DzgMygf8zs6xgBTMbD4wFRgOnAruBf5hZ05D1\nPAQMBi4D+gEdgeejbl2JuoiIiIikoZTPo+6cGxT63MyuA74ECoG3/OJxwG+ccy/5da4BNgOXALPN\nrDXwfeBK59wiv85IYLWZneqcW1JtAErURURERCQNpUOPeri2gAO2A5hZF+Ao4LVgBefc18C/gDP8\nor54HzpC66wFPg+pE5kSdRERERFJQ2mVqJuZ4Q1hecs5t8ovPgovcd8cVn2zvwzgSOCAn8BXVycy\nJeoiIiJST0aOHEkgECAQCNCrV69Uh9MorV69mszMTFatWhW9coqlVaIOTAV6AFfW2xaVqIuIiEg9\nys3N5emnn+aee+6Juc28efMoLCwkKyuLzp07M2nSJA7VIod5/PHH6dGjB1lZWXTr1o2HH364Sp1N\nmzbxy1/+koEDB9K6dWsCgQBvvvlmzNuojnOO++67j65du5KVlcUpp5zCs88+G3P7AwcOMH78eDp1\n6kSLFi04/fTTWbBgQZV67733HmPGjKFv3740bdqUJk2aRFxf9+7dGTx4MBMmTIh7n+pLyseoB5nZ\nw8Ag4GznXHHIok2A4fWah/aqHwksD6nT1Mxah/WqH+kvq9Yt995Lm6eeqlQ2YsQIRowYEdd+iIiI\nHE5mzZrFrFmzKpXt2LEjRdEcHrKzs2uVZ7zyyisMHTqUgQMH8vDDD7Ny5UruvPNOtmzZwiOPPBK1\n/aOPPsqNN97I5Zdfzk9/+lMWL17MzTffzN69e/n5z39eXm/t2rXcf//9nHDCCfTq1Yt33nknrv0L\nd+utt3Lvvfdy/fXX07dvX+bOnct3v/tdAoEAw4cPj9r+2muv5YUXXuCWW27h+OOP54knnmDQoEEs\nXLiQM89/1VNIAAAgAElEQVQ8s7ze/PnzmT59Or169eK4447jo48+qnadN9xwA4MHD2bdunV06dIl\nIfuZFM65lN+Ah4H1QNdqlm8Ebgl53hrYC1we8nw/MDSkzolAGXBqNessAFzRk086ERERiV1RUZHD\nG5Za4BKfE3j/n4uK6nen6sl1113nunTpUqs2PXr0cAUFBe7QoUPlZbfffrtr0qSJW7t2bY1t9+7d\n6zp06OCGDBlSqfyqq65yrVq1cl999VV52a5du1xJSYlzzrk5c+a4QCDgFi1aVKtYw23YsME1bdrU\n3XzzzZXK+/Xr54499lhXVlZWY/t//etfzszclClTysv27dvnjj/+eHfWWWdVqvvll1+6ffv2Oeec\nGzt2rAsEAtWu9+DBgy4nJ8dNnDixlntUd7X5+0n50Bczmwp8D/gusNvMjvRvzUOqPQTcbmbfMbOT\ngZnAF8BcKP9x6ePAFDMbYGaFwHTgbVfTjC+goS8iIiKStlavXs3q1asZPXo0gUBF2jZmzBjKysqY\nM2dOje3feOMNtm/fzpgxYyqV33TTTezatYuXX365vCw7O5u2bdsmNP6//vWvlJaWcuONN1Yqv/HG\nG/niiy+i9trPmTOHjIwMRo0aVV7WrFkzfvCDH/DOO++wYcOG8vLc3FyaNWsWU1wZGRkMGDCAuXPn\n1mJv6l/KE3XgBrwe8YV4PefBW/l3Ic65+/DmWn8Ub7aXLODbzrkDIeu5BXgJmBOyrsuibl2JuoiI\niKSp5cuXY2YUFhZWKs/Ly+Poo49m+fLl1bSsaA9UaV9YWEggEIjavq5WrFhBdnY2+fn5lcpPPfVU\nnHNRt79ixQq6detGy5Ytq7QPLo9XYWEh//73v9m1a1fc60i2lI9Rd87F9GHBOTcJmFTD8v3Aj/xb\n7JSoi4iINFh7Du5hzdY1Sd1Gfod8WmS2SOo2qlNc7P1sLy8vr8qyvLw8Nm7cGLV9kyZN6NChQ6Xy\nzMxM2rdvH7V9XRUXF3PkkUdWKQ/uTyzxV7fvzrk6xd+1a1fKyspYs2YNffv2jXs9yZTyRD3llKiL\niIg0WGu2rqHwscLoFeugaHQRBXkFSd1Gdfbu3QsQcUhH8+bN2blzZ9T2TZs2jbisefPm5etPlr17\n91Ybe3B5MtvXpF27dgBs3bo17nUkmxL10tJURyAiIiJxyu+QT9HooqRvI9lKSko4cKBiRG9WVhat\nW7cmKysLgP3791dps2/fvvLl1cnKyqq03tq2r6usrKxqYw8uT2b7mjjvx8t4l/FJT0rU1aMuIiLS\nYLXIbJGy3u5EuvTSS1m0aBHgJY7XXnst06dPLx/2UVxcTKdOnSq1KS4u5rTTTqtxvXl5eRw6dIit\nW7dWGv5y8OBBtm3bRseOHRO8J1W3v3DhwirlwSE90bZf3fCeWNvXpKSkBKDKsKB0kg4/Jk2tsrJU\nRyAiIiKN3JQpU1iwYAELFizg1Vdf5Re/+AUAvXv3xjnH0qVLK9UvLi7miy++oE+fPjWut7r27733\nHmVlZfTu3TuxOxJh+3v27GHNmsq/I3j33Xcxs6jb7927Nx999FGVH3zG2r4m69atIxAI0K1bt7jX\nkWxK1NWjLiIiIinWp08fBg4cWH4LzpLSo0cP8vPzeeyxx8qHagBMnTqVQCDAZZdVTHC3d+9e1q5d\ny7Zt28rLBg4cSE5ODtOmTau0vWnTppGdnc3gwYOTul8XX3wxGRkZTJ06tVL5H//4Rzp16lTpgkXb\ntm1j7dq1lcadDxs2jNLSUh577LHysgMHDvDEE09w+umnV/mWoTaKioo46aSTaNWqVdzrSDYNfdEY\ndREREUlj999/PxdffDHnn38+V155JStXruSRRx5h1KhRnHjiieX1lixZwjnnnMOkSZOYMGEC4P3o\n8je/+Q1jx45l+PDhXHDBBbz55ps888wz3H333VXmTb/zzjsxMz788EOcc8ycOZPFixcDcNttt5XX\nmzRpEpMnT2bhwoX069ev2tg7derEj3/8Yx544AEOHDjAN7/5TV588UXefvttnnnmmUrjw//whz9U\nWeepp57K5Zdfzq9+9Ss2b95cfmXSzz77jBkzZlTa1ueff86TTz4JUP4Nwl133QVA586dueqqq8rr\nlpaWsmjRIsaOHRvjq5AaStSr+YGFiIiISDoYPHgwL7zwAr/+9a+5+eabyc3N5fbbb+eOO+6oUtfM\nqvw48sYbb6Rp06b87ne/429/+xvHHHMMDz30ED/6UdUZrSdMmFDe3szKk2Ezq5So7969m0AgwFFH\nHRU1/nvvvZecnBweffRR/vKXv3DCCSfw9NNPc8UVV0SNHeDJJ5/kjjvu4KmnnqKkpIRevXrx8ssv\nc9ZZZ1Wqt27dOu64445K6wh+YOnfv3+lRH3BggWUlJRwzTXXRI0/lSz0a5TGxMwKgKKi226j4M47\nUx2OiIhIg7Fs2bLgBXQKnXPLErnu8v/PRUUUFDT8H4mGGzlyJG+88QZFRUVkZGTQpk2bVIcUl9NO\nO40uXbrw7LPPpjqUuFxyySVkZGREvbJrMtTm70c96upRFxERkXq0fv16cnNz6dmzJx988EGqw6m1\nnTt38sEHH5QPM2lo1qxZw/z583n//fdTHUpUStQjzM0pIiIikgzjx4/n6quvBqBly5YpjiY+rVq1\nSvqFkpIpPz+/2rnl040S9QbyQomIiEjDl5+fXz6ji0g0mp5RibqIiIiIpCEl6krURURERCQNKVFX\noi4iIiIiaUiJun5MKiIiIiJpSIm6etRFREREJA0pUVeiLiIiIiJpSIm6EnURERERSUNK1JWoi4iI\niEgaUqKuRF1ERETqyciRIwkEAgQCAXr16pXqcCRBSktLOfbYY/njH/+Y0PUqUdesLyIiIlKPcnNz\nefrpp7nnnntibjNv3jwKCwvJysqic+fOTJo0iUOHDsXc/vHHH6dHjx5kZWXRrVs3Hn744Yj1duzY\nwejRozniiCNo2bIlAwcOZPny5VXqvfrqq/zgBz/g5JNPJiMjg65du8YcS002btzI8OHDadeuHW3a\ntOGSSy5h3bp1Mbdfs2YNF154Ia1ataJ9+/Zcc801bN26NWLdWI7Jpk2b+OUvf8nAgQNp3bo1gUCA\nN998s0q9jIwMfvKTn3DnnXdyIIGdwErU1aMuIiIi9Sg7O5sRI0YwaNCgmOq/8sorDB06lJycHB5+\n+GGGDh3KnXfeyc033xxT+0cffZRRo0Zx8skn8/DDD3PmmWdy8803c//991eq55xj0KBBPPvss+XL\nt2zZwoABA/j4448r1X3mmWd49tlnadu2LZ06dYptx6PYvXs3AwYMYPHixdx+++1MnjyZ5cuXM2DA\nAEpKSqK237BhA2effTaffPIJ99xzDz//+c95+eWX+X//7/9RWlpaqW6sx2Tt2rXcf//9bNy4kV69\nemFm1W5/5MiRbN26lWeeeSa+AxCJc65R3oACwBUde6wTERGR2BUVFTnAAQUuWf+fi4rqd6fqyXXX\nXee6dOlSqzY9evRwBQUF7tChQ+Vlt99+u2vSpIlbu3ZtjW337t3rOnTo4IYMGVKp/KqrrnKtWrVy\nX331VXnZc88958zMvfDCC+VlW7Zsce3atXPf+973KrUvLi52paWlzjnnLrroolrvUyT33nuvCwQC\nlV77NWvWuIyMDHfbbbdFbX/jjTe67Oxs98UXX5SXLViwwJmZ+9Of/lReVptjsmvXLldSUuKcc27O\nnDkuEAi4RYsWVRvDd77zHde/f/8a46zN34961GvxtZGIiIhIfVq9ejWrV69m9OjRBAIVaduYMWMo\nKytjzpw5NbZ/44032L59O2PGjKlUftNNN7Fr1y5efvnl8rLnn3+eo446iqFDh5aXdejQgeHDhzN3\n7lwOHjxYXn7UUUfRpEmTuu5eJc8//zzf/OY3KSgoKC878cQTOffcc5k9e3bU9i+88AIXXXRRpR7+\nc889l27dulVqX5tjkp2dTdu2bWPeh/PPP5+33nqLr776KuY2NVGirkRdRERE0tTy5csxMwoLCyuV\n5+XlcfTRR0ccPx7eHqjSvrCwkEAgUKn98uXLKyXJQaeeeip79uzho48+inc3onLO8cEHH9C3b9+I\n2//444/ZvXt3te03btzIl19+WW378P2E2I5JbRUWFlJWVsY///nPuNcRKiMha2nIwsYsiYiISMOx\n59Ah1uzZk9Rt5LdoQYsE9x7Hqri4GPAS83B5eXls3LgxavsmTZrQoUOHSuWZmZm0b9++Uvvi4mL6\n9+8fcTvgJcMnnXRSrfchFtu3b2f//v3V7mdw+yeccELE9tGO0/bt2zl48CCZmZm1Oia1FfxR7apV\nq2L+DUJNlKirR11ERKTBWrNnD4VFRUndRlFhIQWtWiV1G9XZu3cvAM2aNauyrHnz5uzcuTNq+6ZN\nm0Zc1rx58/L1B+tWtx3nXKW6iRZtP0Pr1KV9ZmZmrY5JbbVr1w6g2plmakuJuhJ1ERGRBiu/RQuK\nwoYwJGMbyVZSUlJpWr+srCxat25NVlYWAPsjTCe9b9++8uXVycrKqna6wPD2WVlZ1W7HzKJuqy6i\n7Wdonbq2r80xqS3n/SC6xtlhakOJellZqiMQERGROLVo0iRlvd2JdOmll7Jo0SLAS/KuvfZapk+f\nXj6Uo7i4uMo0iMXFxZx22mk1rjcvL49Dhw6xdevWSkM9Dh48yLZt2+jYsWOlusEhJOHbASrVTbSc\nnByaNWsW9/ZDj1Ok9jk5OWRmZpbXjfWY1FZwGsnwYTXxUqKuMeoiIiKSYlOmTKk0V3gwWezduzfO\nOZYuXVrph5LFxcV88cUX3HDDDTWuN7T9hRdeWF7+3nvvUVZWRu/evSvVfeutt6qs491336VFixZ0\n69Yt7v2Lxsw4+eSTWbp0aZVl//rXv+jatSvZ2dnVtu/YsSO5ubkR2y9ZsqTKfsZ6TGoreHGm7t27\nx72OUJr1RUNfREREJMX69OnDwIEDy2/5+fkA9OjRg/z8fB577LHyYRUAU6dOJRAIcNlll5WX7d27\nl7Vr17Jt27bysoEDB5KTk8O0adMqbW/atGlkZ2czePDg8rJhw4axefNmXnjhhfKyrVu3MmfOHIYM\nGVLeI50sw4YN47333mPZsmXlZWvXruX1119n+PDhlep+8sknfPLJJ5XKLrvsMl566SU2bNhQXvba\na6/x0UcfVWpfm2NSW0uXLiUQCHDGGWfEvY5Q6lFXoi4iIiJp7P777+fiiy/m/PPP58orr2TlypU8\n8sgjjBo1ihNPPLG83pIlSzjnnHOYNGkSEyZMALwfR/7mN79h7NixDB8+nAsuuIA333yTZ555hrvv\nvrvSHOHDhg3joYceYuTIkXz44Yd06NCBqVOnUlZWxqRJkyrFtHLlSubNmwfAf//7X3bs2MFdd90F\nwCmnnMJFF11UXvcb3/gGgUCgSmIdbsyYMfzpT39i0KBB/OxnPyMjI4MHH3yQvLw8fvKTn1SqO3Dg\nwCrrvPXWW5kzZw4DBgxg3Lhx7Ny5kwceeIBTTjmF6667rrxebY4JwJ133omZ8eGHH+KcY+bMmSxe\nvBiA2267rVLdBQsWcNZZZ5X/qLTOol0R6XC9EbzyGThXVlbjFaRERESkgq5MGr94rkzqnHNz5851\nBQUFLisryx177LFu4sSJ5VcGDVq4cKELBAJu8uTJVdr/+c9/dt27d3fNmzd3J5xwgvv9738fcTtf\nffWVGzVqlMvNzXUtW7Z0AwcOdMuWLatS74knnnCBQCDibeTIkZXq5ubmurPOOium/dywYYMbPny4\na9u2rWvdurW7+OKL3ccff1yl3je+8Q3XtWvXKuWrVq1yF154oWvZsqXLyclx11xzjfvyyy8jbivW\nY2JmEfezSZMmlert2LHDNWvWzM2YMaPGfazN34+5kK9RGhMzKwCKioCCgwchQ18uiIiIxGLZsmXB\ni8UUOueWRatfG+X/n4uKIl58p6EbOXIkb7zxBkVFRWRkZNCmTZtUh5RUq1atomfPnsyfP7/SePDD\n0UMPPcQDDzzAxx9/HHGayKDa/P1ojDpo+IuIiIjUm/Xr15Obm8vZZ5+d6lCSbuHChZx55pmHfZJe\nWlrKQw89xB133FFjkl5b6kYGb+aXBB5UERERkUjGjx/P1VdfDUDLli1THE3yjRkzhjFjxqQ6jKTL\nyMjg008/Tfx6E77GhkhTNIqIiEg9yM/PL5/RRSQaDX0BJeoiIiIiknaUqIMSdRERERFJO0rUQYm6\niIiIiKQdJeqgWV9EREREJO0oUQf1qIuIiIhI2tGsL6BEXUREJM2sXr061SGIJEVtzm0l6gAXXADr\n1qU6ChEREYGtgUBg31VXXdU81YGIJEsgENhXVla2NVo9JeoASZigXkRERGrPOfe5mZ0IdEh1LCLJ\nUlZWttU593m0ekrURUREJK34CUzUJEbkcKcfk4qIiIiIpCEl6iIiIiIiaUiJuoiIiIhIGlKiHuRc\nqiMQERERESmnRD1IVycVERERkTSSFom6mZ1tZvPMbIOZlZnZkLDlM/zy0Nv8sDrNzOwRM9tqZjvN\nbI6ZHRFzEAcPJmhvRERERETqLi0SdSAbWAGMAaobg/IKcCRwlH8bEbb8IWAwcBnQD+gIPB9zBErU\nRURERCSNpMU86s65vwN/BzAzq6bafufclkgLzKw18H3gSufcIr9sJLDazE51zi2JGoQSdRERERFJ\nI+nSox6LAWa22czWmNlUM8sJWVaI96HjtWCBc24t3sUSzohp7UrURURERCSNpEWPegxewRvGsg44\nDvgtMN/MznDOObyhMAecc1+HtdvsL4tOibqIiIiIpJEGkag752aHPP3QzFYCHwMDgDcSshEl6iIi\nIiKSRhpEoh7OObfOzLYCx+Ml6puApmbWOqxX/Uh/WbVuAdoA/PCH0LIlACNGjGDEiPDfqoqIiDQ+\ns2bNYtasWZXKduzYkaJoRBoXc2l2oR8zKwMucc7Nq6HO0cBnwMXOuZf8H5Nuwfsx6Yt+nROB1cDp\nkX5MamYFQFHRjBkUjBwJK1dCz57J2CUREZHDyrJlyygsLAQodM4tS3U8IoertOhRN7NsvN7x4Iwv\nXc3sFGC7f5uIN0Z9k1/vXuAj4B8AzrmvzexxYIqZlQA7gd8Db0ed8SXDPwQa+iIiIiIiaSQtEnWg\nL94QFufffueX/wVvbvVewDVAW2AjXoI+wTkXml3fAhwC5gDN8KZ7vCnqlpWoi4iIiEgaSotE3Z/7\nvKapIi+MYR37gR/5t9gFE/XS0lo1ExERERFJpoY0j3pyqEddRERERNKQEnUl6iIiIiKShpSoK1EX\nERERkTSkRF2JuoiIiIikISXqStRFREREJA0pUVeiLiIiIiJpSIm6EnURERERSUNK1Js08e6VqIuI\niIhIGlGi3qQJmClRFxEREZG0okQdIDNTibqIiIiIpBUl6qBEXURERETSjhJ1UKIuIiIiImlHiToo\nURcRERGRtKNEHZSoi4iIiEjaiStRN7MLzexbIc9vMrMVZvaMmbVLXHj1RIm6iIiIiKSZeHvU7wda\nA5jZycDvgPlAF2BKYkKrR5mZcPfd8Kc/pToSEREREREg/kS9C7DKf3wZ8JJz7lbgJuDbiQisXmVm\neve//W1q4xARERER8cWbqB8AWviPzwP+z3+8Hb+nvUHJyPDuAxqyLyIiIiLpISPOdm8BU8zsbeBU\n4Aq/vBvwRSICq1fOefdK1EVEREQkTcSbmY4FSoFhwI3OuQ1++beBvycisHpVVubdK1EXERERkTQR\nV4+6c+5z4KII5bfUOaJUCCbqZqmNQ0RERETEF+/0jAX+bC/B5xeb2V/N7G4za5q48OpJcOiLEnUR\nERERSRPxjvV4FG88OmbWFXgW2ANcDtyXmNDqkYa+iIiIiEiaiTcz7Qas8B9fDrzpnPsucB3edI0N\nixJ1EREREUkz8WamFtL2PLyLHQGsBzrUNah6p0RdRERERNJMvJnpUuB2M7sa6A+87Jd3ATYnIrB6\npURdRERERNJMvJnpj4EC4GHgLufcf/3yYcA/ExFYvdKPSUVEREQkzcQ7PeMHwMkRFv0cOFSniFJB\nPeoiIiIikmbivTIpAGZWCHT3n65yzi2re0gpoERdRERERNJMXIm6mR0BPIc3Pv0rv7itmb0BXOmc\n25Kg+OqHLngkIiIiImkm3i7kPwAtgZOccznOuRygJ9Aa+H2igqs36lEXERERkTQT79CXC4HznHOr\ngwXOuVVmdhPwfwmJrD4Ff0yqRF1ERERE0kS8mWkAOBih/GAd1pk6wR714L2IiIiISIrFm1S/DvyP\nmXUMFphZJ+BBf1nDEkzQD0b67CEiIiIiUv/iTdTH4o1H/9TMPjazj4F1QCt/WcMSTNQ/+wwefTS1\nsYiIiIiIEGei7pxbj3fBo8HAQ/5tEHAxMCFh0dWXP/3Ju9+yBW64IbWxiIiIiIhQh/HkzvOqc+4P\n/m0B0B74QeLCqyeXXw4//3nFc41VFxEREZEUa3g//EyWzMyKx4ca3sVVRUREROTwokQ9KDRRLy1N\nXRwiIiIiIihRrxCaqGv2FxERERFJsVpd8MjMXohSpW0dYkkt9aiLiIiISBqp7ZVJd8SwfGacsaRW\ns2YVj5Woi4iIiEiK1SpRd86NTFYgKZebW/FYibqIiIiIpJjGqAfl5VU8VqIuIiIiIimmRD1IibqI\niIiIpBEl6kEdO1Y8VqIuIiIiIimmRD2oVauKx0rURURERCTFlKgHmcGkSd5jJeoiIiIikmJK1EMN\nGeLdK1EXERERkRRToh4qw5+tUom6iIiIiKSYEvVQStRFREREJE2kRaJuZmeb2Twz22BmZWY2JEKd\nyWa20cz2mNmrZnZ82PJmZvaImW01s51mNsfMjqhVIErURURERCRNpEWiDmQDK4AxgAtfaGbjgbHA\naOBUYDfwDzNrGlLtIWAwcBnQD+gIPF+rKIKJ+sGDtQxfRERERCSxMlIdAIBz7u/A3wHMzCJUGQf8\nxjn3kl/nGmAzcAkw28xaA98HrnTOLfLrjARWm9mpzrklMQWiHnURERERSRPp0qNeLTPrAhwFvBYs\nc859DfwLOMMv6ov3oSO0zlrg85A60SlRFxEREZE0kfaJOl6S7vB60ENt9pcBHAkc8BP46upEp0Rd\nRERERNJEQ0jU648SdRERERFJE2kxRj2KTYDh9ZqH9qofCSwPqdPUzFqH9aof6S+r1i233EKbNm28\nJ36CPmLRIkZcdlkiYhcREWnQZs2axaxZsyqV7dixI0XRiDQuaZ+oO+fWmdkm4FzgAwD/x6OnAY/4\n1YqAUr/Oi36dE4FjgXdqWv+DDz5IQUGB92TfPsjKgtNOS/yOiIiINEAjRoxgxIgRlcqWLVtGYWFh\niiISaTzSIlE3s2zgeLyec4CuZnYKsN05tx5v6sXbzey/wKfAb4AvgLng/bjUzB4HpphZCbAT+D3w\ndswzvoCGvoiIiIhI2kiLRB1v1pY38H406oDf+eV/Ab7vnLvPzFoAjwJtgcXAt51zB0LWcQtwCJgD\nNMOb7vGmWkXRpIl3r0RdRERERFIsLRJ1f+7zGn/Y6pybBEyqYfl+4Ef+LT5mXrKuRF1EREREUkyz\nvoTLyFCiLiIiIiIpp0Q9nBJ1EREREUkDStTDZWTAj38Mr7yS6khEREREpBFToh4uOPPLoEGpjUNE\nREREGjUl6uF27fLu27VLbRwiIiIi0qgpUQ+3f793f9RRqY1DRERERBo1Jerhggm6EnURERERSSEl\n6uEWLPDuW7dObRwiIiIi0qgpUQ930klw6aUVQ2BERERERFJAiXokzZvDvn2pjkJEREREGjEl6pFk\nZSlRFxEREZGUUqIeSfPmsGkT3HcfOJfqaERERESkEVKiHknz5vDppzB+PKxalepoRERERKQRUqIe\nSfPmFY8PHUpdHCIiIiLSaClRjyQ0US8tTV0cIiIiItJoKVGPJCur4vGePamLQ0REREQaLSXqkYT2\nqO/albo4RERERKTRUqIeSWiivnt36uIQERERkUZLiXokStRFREREJMWUqEfSunXFYyXqIiIiIpIC\nStQjadeu4rESdRERERFJASXqkeTkVDzWj0lFREREJAWUqEcSmqirR11EREREUkCJeiQa+iIiIiIi\nKaZEPZJmzSoeb92aujhEREREpNFSoh7Npk2pjkBEREREGiEl6tEoURcRERGRFFCiXh3n4IEHoLg4\n1ZGIiIiISCOkRL0meXne9IyaolFERERE6pkS9ZoceaR3v3lzauMQERERkUZHiXpNOnTw7rdtS20c\nIiIiItLoKFGvSTBR1xSNIiIiIlLPlKjXpH17716JuoiIiIjUMyXqNWneHFq21NAXEREREal3StSj\nad9ePeoiIiIiUu+UqEfToYMSdRERERGpd0rUo1GiLiIiIiIpoEQ9mvbtNUZdREREROqdEvVo1KMu\nIiIiIimgRD2aDh3gP/+BHTtSHYmIiIiINCJK1KNp3x4OHICePVMdiYiIiIg0IkrUo2nTxrv/4ovU\nxiEiIiIijYoS9WgyMlIdgYiIiIg0QspCoxk2DNq21dAXEREREalX6lGPpkkTuPxy2L8/1ZGIiIiI\nSCOiRD0WzZsrURcRERGReqVEPRbNmsG+famOQkREREQaESXqsWjeXIm6iIiIiNQrJeqx0NAXERER\nEalnStRjoaEvIiIiIlLPlKjHQkNfRERERKSeNYhE3cwmmllZ2G1VWJ3JZrbRzPaY2atmdnzCAggO\nfXEuYasUEREREalJg0jUff8GjgSO8m/fCi4ws/HAWGA0cCqwG/iHmTVNyJabNfPuDxxIyOpERERE\nRKJpSFcmLXXObalm2TjgN865lwDM7BpgM3AJMLvOW27e3Lvft68iaRcRERERSaKG1KN+gpltMLOP\nzewpMzsGwMy64PWwvxas6Jz7GvgXcEZCthxMzjXzi4iIiIjUk4aSqL8LXAdcANwAdAHeNLNsvCTd\n4Tw3G1kAACAASURBVPWgh9rsL6u70B51EREREZF60CCGvjjn/hHy9N9mtgT4DBgOrEl6AErURURE\nRKSeNYhEPZxzboeZfQQcDywEDO+HpqG96kcCy6Ot65ZbbqFNmzaVykaMGMGIESMqCpSoi4hIIzVr\n1ixmzZpVqWzHjh0pikakcWmQibqZtcRL0v/inFtnZpuAc4EP/OWtgdOAR6Kt68EHH6SgoKDmSi1b\nevc7d9YlbBERkQanSucVsGzZMgoLC1MUkUjj0SDGqJvZ/WbWz8w6m9mZwIvAQeBZv8pDwO1m9h0z\nOxmYCXwBzE1IAG3bevfFxXDppfDZZwlZrYiIiIhIdRpKj/rRwDNAe2AL8BZwunNuG4Bz7j4zawE8\nCrQFFgPfds4lZuLz4NCYyy/37g8dgrmJ+QwgIiIiIhJJg0jUnXMjYqgzCZiUlABatKj8/J13krIZ\nEREREZGgBjH0JeXMKj/fsgX+/OfUxCIiIiIijYIS9XiNGpXqCERERETkMKZEvS7M4G9/S3UUIiIi\nInIYUqIej3HjKh4/+WTq4hARERGRw5YS9VgdfXTF4/z8isetWtV/LCIiIiJy2FOiHqv//rficWhy\nrkRdRERERJKgQUzPmBaaNYP//Af274ePP64oV6IuIiIiIkmgRL02jj/eu9+ypaIsfI51EREREZEE\n0NCXeIT2opeWwpdfwquvpi4eERERETnsqEc9Hq1bVzw+cAC+/W1YtgycS11MIiIiInJYUY96PEIT\n9e3bYcUK7/GhQ6mJR0REREQOO0rU43HEEfDYY5CXB1OnQlmZV75rV2rjEhEREZHDhhL1eJjBqFHQ\nsmXl8h07UhOPiIiIiBx2lKjXRWlp5edff52aOERERETksKNEvS6UqIuIiIhIkihRr4vwRP2///Uu\niCQiIiIiUkdK1OsimKiPG+fdX3stXHVV6uIRERERkcOGEvW6CCbqN97o/cAU4OWXq9Y7dKhiZhgR\nERERkRgoUa+Lgwe9+5YtITvbexyIcEi7doUePeovLhERERFp8HRl0roI9qhnZ0Nmpvc4UqL++ef1\nF5OIiIiIHBbUo14XwUS9RYuKBD14f+WV8LvfVa5fUlJ/sYmIiIhIg6ZEvS6CiXpmJjRp4j0OjlV/\n7jn42c8q18/JqXl9n/3/9s48To6i/P/v6rl2Npv75AiHyFdOORI84IvIoYB8A19QDokEQfwh4gGi\nIFdAUAQRRA4R5VIwcp9+QQ4BuUETLgMBkhBy35s9Z+fofn5/PNPpmdnZzSYs2Z3keb9e9eruquru\nerp7dz5V9VTVhzB5Moj0bjkNwzAMwzCMmsOEem/gXCTUK11fKgeRnnQSLF1a/TonnAAXXwyZzJrv\nKQLbbgtPPLH25TUMwzAMwzD6PSbUe4uuhPo555Qf33QTPPccfOlL8Prr5Wm5XPm2O3I5nbf9vPPW\nrbyGYRiGYRhGv8aE+kfh9NOj/Uof9ZDLLut83owZ8OSTcNpp5fG+r9ueCPVwYaVw5hnDMAzDMAxj\ng8KE+kfhyisjf/JQoC9dCtde2/15H3yg2wULyuNDoR6K8GXL4IgjoLW18zVMqBuGYRiGYWzQmFDv\nLULXF4Dvfz/aHzmyc95Zs3Q7c6amP/64HoeDU0MR/oc/wP33a+t7JR0d5eesKx0dWg7DMAzDMAyj\nX2FCvbcYPrx6/KpVsNde5XGhUAdYvlxniIHOLephK321WWB6q0X9pJN0UKphGIZhGIbRrzCh3lvc\ne2/1+Hwehgwpj5s3r/x4zBjdVgr1cKrHtrbO1+0tof7KK7otnZ1m2rTIPccwDMMwDMPoE0yo9xab\nbw7771897aSTYMcdy+OGDNHW9J131lZ3iNxYli1TIR66t6xYEZ0XCvTeEurhiqrt7VHcySfDpZd+\ntOsahmEYhmEYHwkT6r3JL39ZPX70aLj++vK44cOj8OCDumpp2Kp90EHw5S9HAn35ct1OnQp1derT\nHor4jyrUk0ndlg5YXb4cWlo+2nUNwzAMwzCMj0S8rwuwQbHHHvC738F3v1sen0qpwAb43vd0BdJx\n4/R4wACd/WWHHSLxDfDss7DZZrq/fDksXAjjx+vxU0/Bppvq/kcdTBq2qJe616xa1bNFlwzDMAzD\nMIyPDRPqvc0225Qff+MbsNtuKn4//Wn48Y9hyy2j9NDtZfHiztdauVK3y5erO0xI6dzsH7VFPV78\nBMIW9SCApqZyV5iNhVwO7rsPjjmmr0tiGIZhGIZhri+9zuc/X358xRU6KHToUHjjjXKRDjrvele8\n+qpuly+vPpc6dF4c6dRT4fLLy/3auyN0fQlb1JubdZaZjbFF/Yor4Otf18G0hmEYhmEYfYwJ9d5m\n4ECYMAGuuUYF76hR3ef/xCe6Tmts1PnZly+PhPTQoeV58vkoTURdb848U11pekLo+hJWBMIW/o2x\nRT3stdgYKymGYRiGYfQ7TKh/HDz0kPqi94S//rX79L32UqG+ZIkeV7bIA0yfrttw0CloS/0776z5\n/pU+6qFQ706sXnEF7L77mq/dFddf331PQl9RucqsYRiGYRhGH2KKpK8ZOrR6q/pOO+l2333Vf33S\nJD0ePLhz3v/8R7fvvVcev8MO0TSOXVHZot7YqNuuhPpbb6mf/WuvVU//97+hvl5daKrR1qaDbU86\nqfty9QXVFpYyDMMwDMPoI0yo9wcaGjrHvfWWCsdKF5aDDy4/3nJLeP993a+2SNG773aeGeacc2Dr\nrXW/clGlUteXXK58sOpbb+mA2O64/noV+R9+2DnN9+EnP9H9cKBsfyIU6pV+/4ZhGIZhGH2ACfX+\nwH33wdlnw3XXdU4bMaL8+Kyz4IEHouNttoHnn9fFln7zm87nP/SQtpq//bYK0PPP1/ne58zR9FCU\nhvOml7q+jB5dXlFYk0iHqGU+FL3PPAN//7vu3313NJ98pRh+/nmdxrKrQbPrg3Ae+9JpMg3DMAzD\nMPoIm56xP7DNNnDJJbp/6qnlaTvtpPOz/+tfUVx9fbQ/aFC5cK/k/PN1+8EHcPvt5YsyiUSC+cwz\ntZW9tEW9UNDjpqbqPum5nAr84cN1NdODDoqE9uzZOg/8vvtG93r22fJzS7n7bo178UVd7CnknnvU\n937iRB2o+3ESVi66Euoi+j6uvrrrVWgNwzAMwzB6CWtR7++MGhVN0xgyYEC0Hy6ctCaam+Hll8vj\nQveWkCOPjHzUS91lbr1VhXclEydGLf5/+AMccUQk1A8/HDbZpPxeYSUAOs//HuYNxfzjj2tr/JFH\nwimnaIXk4+KFF2DevEioV/r1B4G67MycqT0TZ59d/TpXXglTpvReuTIZWyHWMAzDMDZiTKj3N26+\nubz1vBqlLepnn10u3Lti+XL1V58wIYpraFCXk1Lmz+987mmnVb/mPffotlTYlq5wWirG33xTW+ZD\ncjl47DGdd37GjMin/emndXvggVFrfE+YOFErC2tLPg///d/6XLpyfXn3Xfj1r6OZfLryYT/jDC1H\nT7j99mh8QD4f3buU3Xb7eCsooN/FuedWv39PuPFGXSnXMAzDMIxex4R6f+OEE2D8+O7zhEJ94ECd\nZ33kyM55FiyI9jfdFH7wA1i4sFyoV+PNN7tPr/SZB6irq37fUmbP7izUzzlHW/m33x5+/3uNf/XV\nrluRuxOTU6ao+w1oq/411+jg1WrkcuoutHAhvPKKxrW0RPkrhXo4p3xYrjUNNu3qvkuXRve7+OLo\nXskkHHVU5/zvvtv9fXqDc85Rt6sZM9bt/G9/29yADMMwDONjwoR6rXDppZHLRSjUw8WUSv3HTzhB\nWzg33VRbqRctKhfXhx3WeRrHUqZOjVp6q3H88d2Xc/Hi6vHnnqvCOGTlSvjkJ8vzbL+9itzTT69+\njXBBIoCXXoqEc+XMOEcfrRWTapUOER20evjh6kP/+usav/nm0ZSUoVCfN0+fxWOP6XEo2KsJ9dLp\nLMNZeCrZZx/43Od0v3KWnXvvrX7Omsjl4K679PnOnl19iskPPui6ktObizvdc0/n8RLTp+tgacMI\ngmjNB8MwDKNHmFCvFc46Kxpwmk7rNhTqt96qwnXFCm2ZDl1GttgCxozRwZ4A552n5wwb1v29uptP\nfPPN4dpru073vPIW9pA5c8qnj2xtVYFZyk47qRvKTTdVv3Yo9H0f9twTTjxRj0sXdho1Ch55RPeb\nmtQ3Pwh0kaZVq6IW7ZD779fttGlRJSOb1bxbbKHHd96p21DUVhPqixZF+3ffXb38Yav1HntEPQ9v\nvRWlP/101yvCNjfD5Mla+WpsjGw+80ytmFxyiQ5KTqcjV6WrrtIelE98An772+hZ3HOPVthEIvek\ntZ1tp6mp82Jdl18ezeoTstNO8NWv6v5TT+k4hsWLdVyAsXFxzTX6Pcyb19clMQzDqBlMqNciod9y\n2MI+cKC21A4bpm4UlYSCfqutdDtkSPfXP/zwrtPa2mC//bpO32yzcqF+2GHVKwaf+pRuTzhBF0kC\n9c3fcccoz/nnl4v2d95RobliRXRc6R5S2eo+eLAK1h//GHbZRX3iSwn9q1tb4ckndf/MM+HPf47y\nhC3s4cqv8+bpoFLQee1vu628t2DyZLVp0SKteDgXzVsPkb1QPlB4v/10vMGLL8Lf/hbF+776yF98\nsb7DYcO0F+Gvf418+kOyWRXloD0T4XXeeEOfxUUX6QDdhx5SwR8OGu7K3UhExXfpcw1tPPbY8nzv\nvVe+Om44BWiYfsIJWjHaZBN9Lr0xFWd7u1bCunI3ArW5q16OnpLP24JYPSUIOg8Wh2hhttJvxDAM\nw+gWE+q1SCKhomFN/uYh3/++bnfbTbexWHn6BRdoK+f06drS21WLMGhrctiiX22g42ablYumvfdW\nYRqWIWTyZN22tenMNf/7v3DZZXDIIVGeL35RW/BDJk7UqRuXLNHj2bNhu+10v9qzCAXxLbfodu7c\n8vS99+7SzLI56cMZb8JFmtJp7Q1YuFDniJ80qfO0mo8/ru5HYctxqWgt5bzzOsfttVe5Pc3N1X3I\nX3ml+iJXoJWNUsJegD/9KYp74YVIUB1wQHnPBKj70Oab60qyn/2sivvbbtOKR6UwXr5ceyFKRdiF\nF0b7LS2dXaoqBy6vXAk771x9hqGuuPVWrYSVVmxK6ejQ7/uuu/RbCwdPDxsGxx3X8/tst50OnF0b\nVqwoX8H3/fejFXszGe39+tvfNrwKwCmnVG8wCP/v9OViZ2+8od9hacXaMAyjPyMiG2UAdgdk6tSp\nslHQ0VF+rPJAw9FHd84fph18sMiwYSIjRoj84hciq1aJLFmiaWPHikycKHL33SL33adxw4eL1NdH\n5995p17vqafK79nWpttf/rLzvX/3O02bOlWksVFkm23Kz91ii/JjEHn77c5xn/5057jScPvtuv2v\n/+o+X2nYdFORL3xB5JBDRG69tXP6n/+s27326vk1S8Mhh3SOe/rp6nmPPLLn1/3MZ9acZ9ddRRYs\nqP6NhOGLX6we//zzuh0wIDp/l11EPvEJjZ85U2TrrcvPeeIJzRcEIm++KfLIIxr/wx+K3HyzfiNP\nPinS2hpd8913Rc44Q8T3RaZPF/n97/Wcyy/X9DvvFLnuOpHzzxcZP15k3jxNP+44kQkTdD+bjcpQ\nyqpVIjvuKPKf/4hMmSLy3e9G8SByxBHd/43NnavnXHWVyE03iWy7bfk9QORLX9L9n/88KsM//lH9\neo2N+mzmz9cy95RZs0Qef3zN+RYsENlhB5FFi3p+7Z4Q2hUE5fHf/a7G/+UvIo8+2jl9bXj0UZHH\nHlv78849V8vwyCPV0995p/qznjVLZNmytb/f66+LfO5za/f+aoSpU6cKIMDu0sPfXQsWLKx96PMC\n9JnhG5tQr6RUMF17bef0U04ROe883e/oEMlkorSWFj1vhx2iuCAQ2WQTFfODBmn6t74lUihoej6v\n1ysVSM3NKrgqCQKRf/2rPO6ii0R22qm83KWhsVFFWigGqgXPKz/2fRWed98tcuGFnfOfdprIdtuV\nx+23n8ihh3Z9DxGRvfeOjr/xja7zhuEHP4j2q5UjDKHoDUOl8K0WTj1Vt6nUmvOG4eSTRcaNq55W\nrSIBIhdcEO1nMipMEgmR73xH4156qXP5b75Zn9ctt5RfwzlZLa5B5Ktfjb6Dz39e4376U91+73tR\n3srvGkRee023sVgU98EH5e9r8WLdPvmkxpVWgIJA5I03dH/06EhcNjerIC8URFauFNl++66fZ0uL\nyNKluj9okFZaTjghSr/tNq38zpypeUWiysTFF0fvREQrKkcdJXLZZZ3/bkLGjIlsq6SlRf9OlizR\nCg1oZUdEJJcT+frXRd5/P8r/73+LHHOM5t95Z5HJkzW+vV0r2W+9JTJjhsgzz2jlpvQdLFumdl52\nmTYGfOtbGh9+W11VUErx/eiZL1mi7z2fL39/XTFqlMjVV5fHnXaannfvvZ3zh9/K6afrcwr/d4U2\njR695vJWctRReu5FF2lldn2ycqXIH/7w0SpE3WBC3YKF9RP6vAB9ZrgJdVktVteWQkFWi8BqhOLi\nww87px1zjKzxB7Y73npLOgmhiROjH6Mg0BbRyjyhKAyPn322/LpnnNH5nD/+MdrfZRdZLZz+8IfO\neUuFQyhIxoyJKgMg0tBQ/ZwgEPnTn1S43HBD19f+85+15bk7kX388eXHvi9y7LGd8112WffXqQzP\nPafbUaNEksnu8957b2THQw/p9pxzyntGRo2KRF8o/rurAL38subddVc93mQT3W6+uW4/8xmRBx7o\nmS1PPBHth5WE22/XVnDQCmiY/uGH5dcN7Tr/fN0++KBWdLu735tvRr0iY8d2Tv/Zz0Ticd1PJNTO\nGTP0OIyHSEiG4Z13NO+zz+q3M3u29kKF6S+8oO+/vV1k//1F7rhDK5pheijohw3T1vUw/uKLo7+L\nM8/UuJNPLv9er7666+8/3J82rfvncsMN0X3a27WSumRJ+d8liJx9tu7/4hd6HFY+QeRvf1O7Slus\n77gjsqeuTuPyeX0W3/ymxl93XZT/nnsiAQ9RRfv006P/KaX2zZ2rvRwi+i3dckvn/1MiKvbDymbp\nO3zwQZH33uuc/8MP9X/r5Mn69/baa9WvK6KVzZAVK7TlXkR7mVau1EpSeM+wrL2MCXULFtZP6PMC\n9JnhG7tQ3203Wf3Dsy68+662wFUjCFRodJVW2lK1LpQK8RUrqucJ03/5S/0RC4X8VltVt/vtt9VV\nZssto3MffTQS6OHzevttzR+2kDY0qAgKxa+IyK9+pcd77KHHoXjabjttVXv5ZRVnILLZZuXlCIVt\neF/QVuSVKyM3iNIf/kr3HhEVJW+8oT/2Iio4QF2SRo5UsSii15s1Kzq3rS1qZS0NgweLzJkTHZcK\npe5CQ4P2xqTT5fEjR2pL45ZbqsgM4zfbrPM1vvSlaL9ULK5LCEX9r3/dOW38eBWE1c7bYQcV0KXi\ndvBg3R5wQHlPUbXwwAPVn2tX4aWXRB5+uGd5r7km2g9FfanrWWXPiHMqXiuvc+KJ0f7+++v38eij\n1e8Z9mJUC6U9WqGw7iocfLDIpEn6TYdC+cYb9bt89lmRww7TuIEDtTxd9fKEfweHHabXhOjv1bno\nb+qIIzqL8NL/FWHYccfoOZ51llaiw7TS7zV0uwL9X3jssepS881virz66prfXfg3mMtFLloTJ3b+\nFl5+WXuEwv91YQXowQcjl0PQnkHQ93vWWVF8Q4NWhHoZE+oWLKyf0OcF6HWD4FTgAyADvAzs0UW+\nDU6oTwm7nnvCypXaOt1PWaMt8+bpD1NXbLaZ/tBWMn+++st3R1ub+hB3dOhzWrhQWydLW+FEVKTd\ncUfn8++5R1YLAxGROXNkCojss095vn/8o3Nr14wZKrheeUVdPhYuLO/1aG+X1T/AEyZoS2LoFx/+\n+Fdy772atueeeq1Kf9nSc595RvdL3YTGjVMxER7feKPaE/pgg/YahHaDiteLLtJrhmMaQP3P587V\nby90cRk0SMdAhIJr2TKRfffV40su0WdfTeiUtjb3JGy7rd6rwh1pSuUYiGrhqKMiAddd+L//U5ek\n0t6TE07oPM5iTeHKK9cuf6k93aWffXa52021Z5hO67c/bFjP7rn99vp30JO81caXVIY99xQZOjSy\nZ8iQcneljxIGDlzzcy6t6HQXSt3Juhq70d27aW9XV6Ottuq+8nP44bodO1Yr4HfeqcdHH91zu8NK\ney9iQt2ChfUT+rwAvWoMHA10AJOA7YAbgJXAiCp5dwfkmZeel0whL/7H5Me3PpkwYUJfF6HX+Mi2\ntLaWD0JcnyxerC3iYa9CR4dMiMfVB7onNDV1nw7l4wNE1D+4K5/ftjYVFfffXz39wANFfvxj3c/l\nVBx/+GHkZhO6DYUtnE1NMuGQQ7RnJHQBCQcvvvVW9S77Vas6+8qGLeTf/76KM9DKj0gkXEKXl2ru\nRqH/e6W7Txi+/nXdhq3g48eLfPazUXqxRXTCPvtEcaGNm26qriUnnqiC9b33dNxG6fUPOija33xz\nvX4pL70kctJJ1ctWGq6/PrpeKJw/97nyPCedpL051XodKsYfTADt9ajWov3EE/quw+P//EddLbor\n3xe+oNv999eehNJenKOPjlp6//KXKP5HP+p8naefVneQW24pbwleQ5gQj+s7HjKk+4Hfkyb17Jo9\nqSyEYcSIqJW+NGQykftVZSh1LyoNhx2m76ar8IMfaA9JpbtMZah0Ebv2WnUB2nrr8l6HKVPK8/Qy\nJtQtWFg/oc8L0KvGaAv6b0uOHTAfOLNK3t0B4YYbhKefjsJTT4r7x2OSevJhGfzkfTL2qftll+f+\nTw7+93PynXemy+TZs+XKuXPlloUL5YFly+SfjY3yRkuLzM1kpCWflyXZrDR25RLyMWNCvf/Sq/bc\neqv6I68PSrvMfX/1zBer7Vm8WAXzusxqkcuJ/POfem44HuCvf9W05mZteS9l8mR1I5o3LxpYCOra\n8PLLun/llZGbzrXXavmXL1cxPGtWuchZsUKkUFBbwlbK3/9e5O9/V/chEa1chBWMcFaawYN1YOGV\nV6qI32abrm1cujSqhBx3nMhdd+lA6XHj1KXp73+P8ixcGPVogPYYOafvO2TuXK0wvPCChuef14pd\npVB/9dXIPas0LF4cuUWEFbdZs9Sm3/xGe5D22CPKf8cd+izuv18HmTY3a2v7AQdoy3wp+byW7cUX\n9fjxxyPXpdB9pZTwHjfe2K1b04TSfOHsUSefrJXJMO2KK/SaTU1avu5mOQpn8QlDKtX12JBRo/Qb\nevppHewbxotor9ell5YPHgf9PsMeoYcfjr6t5maZEPZCfe1rOrj65pv1PV58sT7b8JsLbSytsO25\nZ+cekGuuKX+m4fihXXfV4xtv1OMTT+z6G11HTKhbsLB+ghOR3pvrsQ9xziWAduCrIvJQSfytwGAR\nObwi/+7A1E1OuYyOQVvR0u5TEB/q2qkf2Yo3JAv1AR2JAoVYHBIDIT4QLzEI4gMJvCrzBJcw2BMC\nHJum0mydirMsl2FkPE47cTaLC5vW1fN68wrqYykOG7UJM5qXEfdi7Dh4tP4LDjpoSA5kZNxjeWYl\ng9LDaXCwRXoAQ2KOBdl2RqUa8IF6z2NlocA3jziC6/96My0Soy6eZkAsxphkksW5HDHnSHseTYUC\nqwoFRsQdPjE2S6VwxfmtfRFiztFcKNBUKDC2uHCRiOCcW70tJR8EZIOAdCxGzDk6fJ+k57Esnyfu\nHMMTCQCW5XIMjMWoi8XI+D6tvr86rblQoM7zqCuZ333CoYfy8EP6GnNBgAfEvfJp/1sLBTznSDhH\nwvMoBAEx5zqVEWBVPs+geByvStrHTSDCYYcdttqeUtp9n7TnVS1zf+bQQw/loSr2fCRWrlzzqrml\nzJun88XffLPObb9qVbSY14wZuqhW5XO991742tfghhvg//0/oGjLPffoQlHf+160TkAlInq/Aw/U\needB54f3PF2oqjtmzNA56Rsaus+3YgWMGKELmL34Yufyd0WYr7GRQydNit7NokU6n/9PfqKrgk6a\npDZcfrku9hWuYhwEagfoIlhbbaVrKrS3d/08ekIQ6PzpsVi0uFbIbbdp+vHH6/H48TB1Kvz859H6\nArvtxqEzZ/LQ/vvDffd1fh7hceXvmO/r2hCf/KTOpZ9O6wJow4fre7jmGnjuOV2E6+CDo7JedBH8\n7Ge6mNeUKfpeSxfm+sc/9B1+9rNRXCaj4cgj9ZkGgX6LL75Yvi4EcOi4cTw0bRo88IAuBtcVzc1Q\nXw8/+pGW9emnYZ99tMy3367f+bbb6urMleti/POfuiDayJF6PH26LnoXHvcS06ZNY9y4cQDjRGRa\nr17cMIzVbEhCfRNgAfB5EXmlJP4y4Asi8vmK/LsDU6dOncruu++OiP4uPfooPPOMrsXy2mv6P1ri\nbTDyHRg5HQbNp37s+xTGvIbvpZFhTRAbiCTSSCpevLgH6U1BfKjfGupGQb4J4gOBAJLDId4AuUYY\nsLXmFx/8jMavCfHBxTrHn3su/OIXa/fggoJeK8hrOfx2SBQXMvKzmuY84h2tFFL1UMjguQQuyOPH\n6yCmYtvl2/EE/GQ9XiFH4MXBgQsKiIuvFgGJjlYKyTrEebggQGLFZyYBXsFHPA8nPsHkC/F+dj7g\nCOJJvMAHv4DEkyQ7mvElS6F+FDgPr5Ajkc+TTaVJZJtIZDrwUwPxnCObSJLKt5KpHwrA4LZGgrxP\nkM+QHTwch4cTqOtopyNdT8rPkc530FLXQMF5xPwsIgGIAy+BJ1nExSh4cRKFHLGCo+A5cDF8fwXJ\nQoCL1eN74KcGksw2k0kPITj/QurOO4NCYRUkhuN5CQpeAj9ZRyzfTsIPSLcvJfA8YkESiaXxPIfv\noOACIIFQIJ5tIumnCTwhSDbg5wvEpB3nAojXI+T0+YkQk3pixOighaQfUAhaSMaHQZAkiCdIn52L\nWAAAEnJJREFUZ5fRmkgS99tJFZK0JvL4ySHEcqsoBG0MoI48EOCI1w2hQA6XdyScx4pLryD9s8kM\nbe+gID7O5WhKDqZOsgQuRSrvAwFZlyde8Em5NHnaScTjFOL1uHwG5+cY4CcJYkma4wkGk8VJmpxk\nEdeO5ztaJAN1A0gGkMznGBYkaC1kafV86uLD8P0Al8zS4gkpSRCPJUgUsnQk6sh5CYa1t+IDIlly\niTTZVANB+xJG+PV01A+goTXL3F//ik3O+Clt/nziiUEM8BK0xQaQ9QRxLdQFSer8GAWgzosTD2J0\nFFrI1iXp8KA+CCgEcQqeUEgMIJ1vJpUvQJDHERB4MfLOIyYxPC+gSQoMjadJe/VkgjypQoDgIblV\nZONpOvwYA5IedQLt5MFL4rwkKQLaA0ciCBhYKJCJxxDP0ZRIEnRk2HTZfAqjBvP+pdex5U9+jMSE\nnINEUMDloXFAGi/m4a1sxFuwlPSo0dTX10FdkpQ4crk8zTGHn0qRaFqMFPLUDxpOEOQIYiky8TR1\nuWZa6SDu6hggHoGXpC1oJs4Y2hpSNARCfa6FXMEnyHVQSPgkFy9iYP1gUsPG4PCYHxdiQEMhDoWA\nQDpoTiUZkBfyHc3UuQRBMk1H00IGDxjBu7+5iuHnnsXgbA4Xi5NJJMmQJZ1vJ9HSTJs/lPSIwcQC\nSAWOeODhyNIiS0kykFwhhecFFJyPH0sQE8H5EAtayceTpCVJ1oO0ExpdB8PnL6Jx1ECSMxfjRo+i\nbtgQfC9JA454LE47Pr7EiPlCq+RJBpCXPHUxj1RzMx0jR1AXxPFzAfmckIx5JBIBLZlW5lx1Ndt9\n63j8ISNJeUkSsYAl5Kh3MWLBQBJenHY6yAYBWb+JeLaJuqYCTVuMxSu0kc9lGJ0ciB9A4NLE8wXa\ngxYSsXoKXpaENxgPaJCAPB51CAU8PjtiNJftt9fa/TasARPqhrF+iPd1AfqQOoB3KlZjHDdOQ0gm\nA7NmQXu7Ry63MytW7MycOQfT9qEu9tjRAXV10JENiG8ynZZVSQqtQ8mwAvHyNMdnk3ZZMsvHEktl\nyKcXEs87XGoufvNIst475BKN1BdGEU82sSz+PrHcGEaNSpOTNppowDEAL76AIBcnm4gTH+KRz+bI\nZxwMzBPzIN6Rp33+fFK3PE/SJchLI8Q6iNU1QBYgQy7t4TdncV4rXmELgvhi/AbBBeC5AQRJH5GA\nZItH4Mfx43WI6yDmBfgj9B6+n4B4O+J5xPNJqBNockgiAckW4rlW/HRAnOH4uQyxfBJiGQrZDkhD\nPjkMMm24IIlLZ5HWAK9QDw2roD6NkEMowJIFBC88i8OHpgWIVwdeHeJayKaG4+XqIdsOde0Ecch6\nOWgLKNSPoJDykUQ9+G2QaSTDIGgOIL6KpqEjwCUgVg+rloJrB+eRaxgF2WZyAi11DZDvgECfM9TB\ngHrILdW1fP0kZAIKqTikAvAFvBzUxcjH6iEfh5wHdSlyvg8rV8DKhWT+9RIwDJILoWM5LrcKryWG\n3xDDTyfoSI0AH/AKIC0Qi0O+AF4D+KvAT0FyAKRyIAF4dZDPgpfWioTkoBCAK/ZIe3lwAQQeJAZr\nud08iHlacfLS0N4G6SS4OLQXwJsLCcCL0+p3QMHXe8l7EBsMrqAOZW2ttL79Bq2S13vH6iHzH9rq\nR4DfoWVxAjkfYnUQy0OQBC/QPyyX0usmUxDkoGMVy9IjIGjRZ0BCyxmkoLBQV7qMp/gwngQ/D4UO\nkIXFd5nUc7KNQEEru4V2cLC8fiQUsprPz0DQDG4QK+oGQZBVW7IZWlbMAhz4y/S+2eV6j9hQiHsQ\nr1e7PA/QihPZFggEUvUgea305pZB3Vjw4hpEAF9bkx1a8c37LI37+nwEvTboM8wtg0Kb2hRP6vWD\nDvASer1Cq7735FDdD4orywZZFtSNhiXNkM0wffliKGT0Gcfr9Nwlq8CL6bedroNMI+RSWjn3UlpR\nzzWqLanRWra2RfpcCCCe1rh8i9qaaNC/sQBI+7DS0/vlVuo5LllsBIhBtgBLitfqWK7vKBYvfsdJ\naJsPyYH6nhKDIL9Qn3FuCbQ30TxzKgzYVN9BvllbrVMjQAoQfAgtOS1/LK3PysW1rLKo2PgwWM/J\nNxXfTaL4nSzTbVA8P9vIzOQgWNoMg2KQWQXLnT7LRIM+m0KHvlMvqefF0lpuLw54MHeOXgtPW/yd\nV3yGHuTzvOgloalR413xmYkPblXxO/IhyABxfebJhPaMoA0Cc2jWc2jUMkgBpK34rTUV3/kAvaaX\nAAmY+vY0jh7yEXpGqlDy21nXqxc2DKOMDalFfW1dX44F/rJeC2kYhmEYGxYTRWRKXxfCMDZUNpgW\ndRHJO+emAvsDDwE4dfzdH7i6yimPAROBOehMMYZhGIZh9Iw6YCv0t9QwjI+JDaZFHcA5dxRwK/Ad\n4FXgdOBrwHYisqwPi2YYhmEYhmEYa8UG06IOICJ3OedGABcBo4HXgQNNpBuGYRiGYRi1xgbVom4Y\nhmEYhmEYGwremrMYhmEYhmEYhrG+MaFuGIZhGIZhGP2QjVaoO+dOdc594JzLOOdeds7t0ddlqsQ5\nt7dz7iHn3ALnXOCcO7RKnouccwudc+3OuSecc5+sSE85565zzi13zrU45+5xzo1af1asLsfZzrlX\nnXPNzrklzrn7nXP/VSVfrdjzHefcG865pmJ40Tl3UEWemrClEufcT4vf25UV8TVhj3PugmL5S8Pb\nFXlqwpaS8mzqnLutWJ724re3e0WemrCp+H+38v0EzrlratAWzzl3sXNudrGsM51z51XJVxP2FMvS\n4Jy7yjk3p1je551z4yvy1Iw9hlHziMhGF4Cj0SkZJwHbATcAK4ERfV22inIehA6MPQxd0uXQivSz\niuX+H2An4AFgFpAsyXM9OgXlPsBuwIvAc31gyyPAccD2wM7A34rlSteoPYcU3882wCeBn6NLS21f\na7ZU2LUHMBt4DbiyRt/NBcCbwEhgVDEMq0VbimUZAnwA3AiMA7YEDgC2rkWbgOEl72UUOoWuD+xd\ng7acAywt/i/YAjgCaAa+V4vvpliWO4G3gL2ATxT/nlYBm9SiPRYs1Hro8wL0idHwMvDbkmMHzAfO\n7OuydVPmgM5CfSFwesnxICADHFVynAUOL8nzqeK1PtPH9owoluO/NwR7imVZAZxQq7YADcC7wH7A\n05QL9ZqxpygspnWTXjO2FO99KfDPNeSpKZsqyn4V8F4t2gI8DPyxIu4e4M81ak8dkAcOqoj/N3BR\nrdljwcKGEDY61xenK5iOA/4RxomIAE8Cn++rcq0tzrmtgTGU29EMvEJkx3h0Cs7SPO8Cc+l7W4eg\ni3yvhNq2p9j9fQxQD7xYw7ZcBzwsIk+VRtaoPds6dRmb5Zy73Tk3FmrWlgnAv51zdzl1G5vmnDsp\nTKxRm4DV/48nAjcVj2vNlheB/Z1z2wI453ZBW6IfKR7Xmj1xIIYK7VIywH/XoD2GUfNsUPOo95AR\n6D+iJRXxS9Baf60wBhW61ewYU9wfDeSK/0i7yrPecc45tBXteREJfYdrzh7n3E7AS2grVAvagvSu\nc+7z1J4txwC7oj+yldTau3kZ+CbaO7AJcCHwbPF91ZotoO4HpwBXAL8APgNc7ZzLisht1KZNIYcD\ng4E/FY9rzZZL0RbkGc45Hx33da6I3FFMryl7RKTVOfcScL5zbkaxDMeiAvt9aswew9gQ2BiFutH3\n/A7YAW15qmVmALugQuNrwJ+dc1/o2yKtPc65zdGK0wEiku/r8nxURKR0SfP/OOdeBT4EjkLfWa3h\nAa+KyPnF4zeKlY7vALf1XbF6hROBR0VkcV8XZB05GhWyxwBvo5Xd3zrnFhYrUbXIN4CbgQVAAZgG\nTEF7og3DWM9sdK4vwHJ04NLoivjRQC39WCxGfeu7s2MxkHTODeomz3rFOXct8BXgiyKyqCSp5uwR\nkYKIzBaR10TkXOAN4IfUni3j0IGX05xzeedcHh0E9kPnXA5tCasle8oQkSbgPXTQb629G4BFwDsV\nce+ggxehNm3CObcFOij2jyXRtWbLr4BLReRuEZkuIn8BfgOcXUyvNXsQkQ9EZF9gADBWRD4HJNFB\n5jVnj2HUOhudUC+2GE5FZxoAVrti7I/6G9YEIvIB+k+v1I5BwGeJ7JiKtoiU5vkU+gP/0norbHTv\na9EZbPYVkbmlabVoTxU8IFWDtjyJzsSzK9pDsAs6eOx2YBcRCX+ga8WeMpxzDahIX1iD7wbgBTq7\n5X0K7SWo5b+dE9FK4CNhRA3aUo82/JQSUPxtrUF7ViMiGRFZ4pwbChwIPFDL9hhGzdLXo1n7IqBd\n4O2UT8+4AhjZ12WrKOcAVDTtiv7zP614PLaYfmax3BNQofUA6kdYOk3W79Cp3b6Itpy+QN9MY/Y7\noBHYG21ZCUNdSZ5asueSoi1bolOU/RL9cdqv1mzpwr7KWV9qxh7gcuALxXezJ/AEKgiH15otxbKM\nRwf3nY1OB3osOibimFp8P8WyOHT6vl9USasZW4Bb0EGSXyl+b4ej0zVeUov2FMvyZVSYbwV8CZ2q\n9QUgVov2WLBQ66HPC9BnhsN3iz8UGbSWP76vy1SljPugAt2vCDeX5LkQnS6rHXgM+GTFNVLANajL\nTwtwNzCqD2ypZocPTKrIVyv23Ih2BWfQFqbHKYr0WrOlC/ueokSo15I9wF/R6VYzqIiaQsmc47Vk\nS0l5voLODd8OTAdOrJKnZmxCBaBfWcZaswVtTLkSFaVtqGD9GRCvRXuKZTkSmFn8+1kA/BYYWKv2\nWLBQ68GJCIZhGIZhGIZh9C82Oh91wzAMwzAMw6gFTKgbhmEYhmEYRj/EhLphGIZhGIZh9ENMqBuG\nYRiGYRhGP8SEumEYhmEYhmH0Q0yoG4ZhGIZhGEY/xIS6YRiGYRiGYfRDTKgbhmEYhmEYRj/EhLph\nGDWBc+5451xjX5fDMAzDMNYXJtQNw1grnHO3OOeCkrDcOfeoc27ntbjGBc6519bh9raUsmEYhrHR\nYELdMIx14VFgNDAG2A8oAA+v5TVMdBuGYRhGN5hQNwxjXciKyDIRWSoibwKXAmOdc8MBnHOXOufe\ndc61OedmOecucs7FimnHAxcAuxRb5H3n3KRi2mDn3A3OucXOuYxz7k3n3FdKb+yc+7Jz7m3nXEux\nJX90RfpJxfRMcXtKSVrCOXetc25hMf0D59xZH++jMgzDMIx1I97XBTAMo7ZxzjUAxwHvi8iKYnQz\nMAlYBOwM/LEY92vgTmAn4EBgf8ABTc45B/wdGAAcC8wGPlVxuwHAGcBEtEX+L8VrHlcsy0TgQuBU\n4HVgN+CPzrlWEbkN+CHwP8DXgHnA2GIwDMMwjH6HCXXDMNaFCc65luL+AGAhKoABEJFLSvLOdc5d\nARwN/FpEOpxzrUBBRJaFmZxzXwbGA9uJyKxi9JyK+8aBk0VkTvGca4HzS9IvBM4QkQeLxx8653YE\nTgZuQ0X5+yLyYjF93toabhiGYRjrCxPqhmGsC08B30Fbw4cC3wX+7pzbQ0TmOeeOBr4PbAM0oP9r\nmtZwzV2A+SUivRrtoUgvsggYBeCcqy/e7ybn3I0leWLAquL+rcATzrl30db7v4nIE2sol2EYhmH0\nCSbUDcNYF9pE5IPwwDn3bVSIf9s59whwO9rS/Xgx/uvAj9ZwzUwP7puvOBa0sgBaIQA4CXi1Ip8P\nICKvOee2Ag4GDgDucs49ISJH9eDehmEYhrFeMaFuGEZvIUAa2BOYIyKXhglFcVxKDm3pLuVNYHPn\n3CdFZOZa31xkqXNuIbCNiNzRTb5W4G7gbufcvcCjzrkhIrKqq3MMwzAMoy8woW4YxrqQKpltZSjq\n5lKPTtE4GNii6P7yL9R3/X8rzp8DbO2c2wWYD7SIyLPOueeAe51zZwAzge2AQEQe72G5LgB+65xr\nRl1bUqjf+xARuco5dzrqLvMaWrE4ClhsIt0wDMPoj9j0jIZhrAsHoQNIFwIvA+OAr4nIsyLyMPAb\n4BpUEH8OuKji/HtRIf00sBQ4phh/BCrupwDTgcvo3PLeJSJyE+r6cgLaQv8McDwQuum0AGcW7/EK\nsAXwlU4XMgzDMIx+gBOxNUcMwzAMwzAMo79hLeqGYRiGYRiG0Q8xoW4YhmEYhmEY/RAT6oZhGIZh\nGIbRDzGhbhiGYRiGYRj9EBPqhmEYhmEYhtEPMaFuGIZhGIZhGP0QE+qGYRiGYRiG0Q8xoW4YhmEY\nhmEY/RAT6oZhGIZhGIbRDzGhbhiGYRiGYRj9EBPqhmEYhmEYhtEPMaFuGIZhGIZhGP2Q/w8r8hHK\nEZX26wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b44fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   91.000% -- [-1, 1)\n",
      "   97.220% -- [-0.1, 0.1)\n",
      "   95.680% -- [-0.01, 0.01)\n",
      "   94.400% -- [-0.001, 0.001)\n",
      "Loss\n",
      "    2.425  -- [-1, 1)\n",
      "    0.098  -- [-0.1, 0.1)\n",
      "    0.133  -- [-0.01, 0.01)\n",
      "    0.190  -- [-0.001, 0.001)\n"
     ]
    }
   ],
   "source": [
    "uniform_neg01to01_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -0.1, 0.1)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -0.1, 0.1)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -0.1, 0.1))\n",
    "]\n",
    "\n",
    "uniform_neg001to001_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -0.01, 0.01)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -0.01, 0.01)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -0.01, 0.01))\n",
    "]\n",
    "\n",
    "uniform_neg0001to0001_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -0.001, 0.001)),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -0.001, 0.001)),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -0.001, 0.001))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    '[-1, 1) vs [-0.1, 0.1) vs [-0.01, 0.01) vs [-0.001, 0.001)',\n",
    "    [\n",
    "        (uniform_neg1to1_weights, '[-1, 1)'),\n",
    "        (uniform_neg01to01_weights, '[-0.1, 0.1)'),\n",
    "        (uniform_neg001to001_weights, '[-0.01, 0.01)'),\n",
    "        (uniform_neg0001to0001_weights, '[-0.001, 0.001)')],\n",
    "    plot_n_batches=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like anything [-0.01, 0.01) or smaller is too small.  Let's compare this to our typical rule of using the range $y=1/\\sqrt{n}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lJX379mXkyJFcdNFFzZ4zIyOD1157jauvvpo777yT7Oxsxo4dy89+9jMOOeSQ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hAAAC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\ndwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d311e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   96.880% -- [-0.1, 0.1)\n",
      "   96.900% -- General Rule\n",
      "Loss\n",
      "    0.169  -- [-0.1, 0.1)\n",
      "    0.088  -- General Rule\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "general_rule_weights = [\n",
    "    tf.Variable(tf.random_uniform(layer_1_weight_shape, -1/np.sqrt(layer_1_weight_shape[0]), 1/np.sqrt(layer_1_weight_shape[0]))),\n",
    "    tf.Variable(tf.random_uniform(layer_2_weight_shape, -1/np.sqrt(layer_2_weight_shape[0]), 1/np.sqrt(layer_2_weight_shape[0]))),\n",
    "    tf.Variable(tf.random_uniform(layer_3_weight_shape, -1/np.sqrt(layer_3_weight_shape[0]), 1/np.sqrt(layer_3_weight_shape[0])))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    '[-0.1, 0.1) vs General Rule',\n",
    "    [\n",
    "        (uniform_neg01to01_weights, '[-0.1, 0.1)'),\n",
    "        (general_rule_weights, 'General Rule')],\n",
    "    plot_n_batches=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The range we found and $y=1/\\sqrt{n}$ are really close.\n",
    "\n",
    "Since the uniform distribution has the same chance to pick anything in the range, what if we used a distribution that had a higher chance of picking numbers closer to 0.  Let's look at the normal distribution.\n",
    "### Normal Distribution\n",
    "Unlike the uniform distribution, the [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution) has a higher likelihood of picking number close to it's mean. To visualize it, let's plot values from TensorFlow's `tf.random_normal` function to a histogram.\n",
    "\n",
    ">[tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)](https://www.tensorflow.org/api_docs/python/tf/random_normal)\n",
    "\n",
    ">Outputs random values from a normal distribution.\n",
    "\n",
    ">- **shape:** A 1-D integer Tensor or Python array. The shape of the output tensor.\n",
    "- **mean:** A 0-D Tensor or Python value of type dtype. The mean of the normal distribution.\n",
    "- **stddev:** A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution.\n",
    "- **dtype:** The type of the output.\n",
    "- **seed:** A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.\n",
    "- **name:** A name for the operation (optional)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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fI3/G9yAH03vJy5aU0mhE/Ah4QUTsIA9v/yCl9EPyo5eXA98q1pljyevsD8iPZ9Z9sZjv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\nH/e9mPGlRILvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cda4f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.hist_dist('Random Normal (mean=0.0, stddev=1.0)', tf.random_normal([1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the normal distribution against the previous uniform distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FcuxEmj59Oi1atCAlJYXatWszZMgQ0tLSotp33LhxdOvWjdq1axMIBOjTp09C\nYix1yUD58koGREREpPDs3r2bkSNHZikvCRezheW+++7LVzJQErz//vucf/75VK1alTFjxnD++efz\nf//3fwwaNCiq/R988EE+/fRTmjRpQtmyZRMWZ5mEtVxM1aunZEBEREQKT2pqKk8//TS33347NWrU\nSNhxdu3aRXJycsLal/y56aabSE1N5cMPP8zo0lSpUiXuv/9+rrvuOo466qhc958zZw61atXK2C9R\nSt2TgSOPVDIgIiIihcPMuOOOO/j777+zfToQKS0tjeHDh3PkkUeSnJxM3bp1ufPOO9mzZ0+menXq\n1OHcc89l5syZtGrVipSUFCZMmAD4PvKDBg3i9ddfp3HjxlSoUIE2bdrwv//9D4Dx48dTv359UlJS\nOOWUU1i9enWmtj///POM7inJyckcfvjh3HDDDezatSumc/DDDz9w4YUXUrNmTVJSUqhVqxY9evTg\nzz//zIh3x44dTJw4kUAgkKVLzOeff57xGevXr5/xOSPt2bOHwYMHU716dSpXrsx5553HunXrsq37\nyy+/0KdPH2rUqEFycjJNmjTh+eefz9i+ceNGypYty/Dhw7Ps+/333xMIBBg7dmyOn3np0qUsXbqU\nq666KtPYhmuvvZb09HRef/313E8aZCQCiVbqngwcWWsXH38MaWmQlFTU0YiIiMi+rm7dulx22WU8\n/fTT3Hbbbbk+Hejbty+TJ0+mW7du3HTTTXz11Vfcf//9LFu2jDfeeCOjnpmxbNkyevbsydVXX81V\nV11FgwYNMrbPmTOH6dOn079/f8B3wzn77LO55ZZbGDduHP3792fLli088MAD9OnTh48//jhj39de\ne42dO3dy7bXXUq1aNb7++mtGjx7NunXreOWVV/L12ffu3cvpp5/O3r17GTRoEDVq1GDdunXMmDGD\nrVu3UqlSJV588UX69u3Lcccdx1VXXQXAEUccAcDixYvp1KkT1atXZ9iwYezdu5chQ4ZQvXr1bM/d\nyy+/zCWXXMIJJ5zArFmzOOuss7J0x9q4cSPHHXccSUlJDBo0iAMPPJD333+fvn378ueffzJo0CCq\nV69O+/btefXVV7n77rsz7T916lTKlClD165dc/zcCxcuxMxo0aJFpvKaNWty2GGHsXDhwnydx4Ry\nzpWKF9AccE8mn+zAueWLdjgRERHJ2/z58x3ggOYuQf8/z58/v3A/VCGYOHGiCwQCbv78+e6nn35y\nZcuWdde+PzJGAAAgAElEQVRff33G9pNPPtk1bdo04/0333zjzMxdffXVmdq5+eabXSAQcLNnz84o\nq1OnjgsEAu6jjz7KclwzcykpKW716tUZZRMmTHBm5g455BC3ffv2jPI77rjDBQIBt2rVqoyyXbt2\nZWlz5MiRLikpya1ZsyajbMiQIS4QCOR6DhYtWuTMzL355pu51ttvv/3cFVdckaX8vPPOcxUqVHBr\n167NKFu2bJkrU6ZMpmOHzt3AgQMz7X/JJZe4QCDghg4dmlHWt29fd+ihh7otW7ZkqtujRw93wAEH\nZHz+CRMmuEAg4L777rtM9Ro3buw6duyY6+d5+OGHXSAQyBR3SOvWrV2bNm1y3T9STucnJ/n5nS19\n3YROqwPA4pMHwsSJRRqLiIiI5M+OHbBgQWJfO3bEP+66dety6aWXMmHCBH7NYY7z9957DzNj8ODB\nmcpvvPFGnHO8++67Wdrs2LFjtm117NgxUzeT4447DoCLLrqIChUqZCn/6aefMsrKly+f8ecdO3bw\n+++/c8IJJ5Cenp7vO9pVqlQB4IMPPmDnzp352jc9PZ2ZM2dy/vnnc+ihh2aUN2jQgE6dOmWqGzp3\nAwcOzFR+/fXXh5LODG+++SbnnHMOaWlp/P777xmv008/nW3btrFgwQIALrjgApKSkjI9Dfnuu+9Y\nsmQJ3bt3zzX20GcNP5chycnJ+T4XiVTquglVu3cgB36RxuIanbjwim5+RPFJJxV1WCIiIhKFZcsg\noudF3M2fD82bx7/du+66ixdeeIGRI0fy6KOPZtm+atUqAoEARx55ZKbygw8+mP33359Vq1ZlKq9b\nt26Ox4rsbx66KD/ssMOylDvn2LJlS0bZmjVruPvuu3nnnXcylZsZ27Zty+NTZlanTh1uvPFGRo0a\nxYsvvki7du0499xz6dWrF5UrV851399++42dO3dmOR/gE4L3338/433o3IW6F4XXi2xz69atTJgw\ngfHjx2dp18zYuHEjANWqVePUU0/l1VdfZejQoYDvIlS2bFnOP//8XGNPSUkB/ExSkXbt2pWxvTgo\ndcmAGRyTmsTiKhdB5eOhXz9YuBDKlSvq0ERERCQPDRv6i/VEHyMR6tatS69evZgwYQK33nprjvWi\nnXI0twvKpBwGRuZUHrp7np6eTseOHdm6dSu33347DRo0oGLFiqxbt47evXuTnp4eVWzhHnroIS6/\n/HKmTZvGzJkzGTRoECNHjmTu3Lkccsgh+W6vIELx9+rVi969e2dbJ3zth+7du9OnTx++/fZbjjnm\nGF577TVOPfVUqlatmutxatasCcD69eszPdUIlYWeyBQHpS4ZAGjaFN57z+C1p/zthVGj4Lbbijos\nERERyUOFCom5a19Y7rrrLl588UUeeOCBLNtq165Neno6K1asyHRHe+PGjWzdupXatWsnPL7Fixez\nYsUKXnjhBS655JKM8vABxrFo3LgxjRs35o477mDu3Lm0adOGp556KmNl4OwSoIMOOoiUlBRWrFiR\nZduyZcsyvQ+dux9//JH69evnWO+ggw6iUqVKpKWl0aFDhzzjPu+887j66qt55ZVXcM7x/fffc+ed\nd+a5X2pqKs455s2bR8uWLTPK169fz9q1a7nmmmvybKOwlLoxA+CTgR9+gB31m8GgQTBsGKxcWdRh\niYiIyD6uXr169OrVi/Hjx7Nhw4ZM284880ycczz22GOZyh955BHMjLPOOivh8YWeHEQ+AXjsscdi\nWiTtzz//zLLibuPGjQkEApm60FSsWJGtW7dmqhcIBOjUqRNvv/02a9euzShfunQpM2fOzFS3c+fO\nOOd44oknco07EAhw4YUX8sYbb/Ddd99liXfTpk2Z3lepUoVOnTrx6quvMnXqVMqXL0+XLl3y/NxH\nH300DRs2ZMKECZnGLIwdOzYjhpCdO3eyfPlyfv/99zzbTYRS+2TAOViyBFoOHQqvvuqTgunTizo0\nERER2YdEDl4FuPPOO3nhhRdYvnw5TZo0ySg/5phj6N27NxMmTGDLli20b9+er776ismTJ3PBBRfQ\nvn37hMfbsGFDjjjiCG688UbWrl1L5cqVeeONN7JcqEdr1qxZDBgwgK5du3LUUUfx999/M3nyZMqU\nKZPpgrhFixZ8/PHHPProoxxyyCHUrVuX1q1bM3ToUD744APatm3Ltddey969exkzZgxNmjTh22+/\nzdi/WbNm9OjRg7Fjx7J161batGnDJ598wo8//pjl72DkyJHMnj2b4447jiuvvJKjjz6azZs3M3/+\nfGbNmpUlIbj44ovp1asXY8eOpVOnTnmOdQh56KGH6NKlC6eddhrdu3dn8eLFPPnkk1x55ZWZnvx8\n/fXXnHLKKQwZMoR77rkno3zGjBl88803OOfYu3cv33zzDSNGjACgS5cumb47BVEqk4HGjf3YgcWL\noWXLSvD443DRRTBtGkSR7YmIiIhEI7u76UcccQSXXnopkyZNyrL92Wef5YgjjmDixIm8/fbb1KhR\ngzvvvDPTRWKo3Zzu1Oe0LbfykDJlyjBjxoyMfv3JyclccMEF9O/fn2bNmkX1+cI1a9aMM844gxkz\nZrBu3ToqVKhAs2bN+OCDD2jdunVGvVGjRnH11Vdz9913s3PnTnr37k3r1q1p2rQpM2fO5IYbbuDe\ne+/lsMMOY9iwYfzyyy+ZkgGA559/nurVq/PSSy8xbdo0Tj31VN59911q1aqVKc7q1avz9ddfM2zY\nMN566y3GjRtHtWrVaNy4MQ8++GCWz3DuueeSkpLC9u3b85xFKNxZZ53Fm2++ydChQxk0aBAHHXQQ\nd911V5Z1C0LnMfJcvvHGG0yePDnj/aJFi1i0aBHgB4jHKxmw7DLWfZGZNQfmz58/n+bNm3PkkXDu\nuX64AM7B2Wf77GDJEthvv6IOV0REpNhYsGBBaPGkFs65BfFsO/L/ZxEpuPz8zpbKMQPguwplJJRm\nMHo0/PYbdOsGS5cWaWwiIiIiIoWhVCcDixf7hwKAX29gyhT47jto0gR694YffyzSGEVEREREEqnU\nJgMtW8LGjRGTCJ13Hnz/PTzxBHz0kZ9o+OqrE7MUoYiIiIhIESu1ycCJJ/qf//53xIby5aF/f/9U\n4IEH4Pnn4amnCj0+EREREZFEK7XJQLVqflahLMlASEoK3HADdO0KTz4JMay4JyIiIiJSnJXaZACg\nXTv4/PM8Kg0cCD/9BB98UCgxiYiIiIgUllKfDCxb5icRytFxx0GLFjBmTKHFJSIiIiJSGEp1MtC2\nrf+Z69MBMxgwAN5/H374oVDiEhEREREpDEW+ArGZ3Q6cDzQEdgJfArc6577PY7+TgUeAxsBqYIRz\nblJ+jn344f7173/D+efnUvHii+Gmm2Ds2OAqZSIiIhJPS7XGj0jc5Of3qciTAaAdMBqYh4/nfmCm\nmTVyzu3MbgczqwPMAMYCPYGOwDNm9otz7qN8HbxdLoOIQ1JS4F//8rMKDR8OFSvm5xAiIiKSs02B\nQGBXr169kos6EJF9SSAQ2JWenr4pr3pFngw4584Mf29mlwMbgRZATh14+gE/OeduCb5fbmZtgcFA\nvpOBqVPhr79gv/1yqXjNNfDQQ/DSS3DVVfk5hIiIiOTAObfazBoABxZ1LCL7kvT09E3OudV51Svy\nZCAb+wMO2JxLneOBjyPKPgQeze/B2rWDtDSYOxc6dsylYp06cM45fiDxlVf6sQQiIiJSYMELljwv\nWkQk/orVAGIzM+Ax4HPn3JJcqtYAfo0o+xWobGbl83PMRo38mgN5dhUCP5B48eIoK4uIiIiIFG/F\nKhnAjwE4GuheWAc086sRR3V9f+qp0KABjB6d8LhERERERBKt2HQTMrMxwJlAO+fc+jyqbwAOjig7\nGPjDObc7tx0HDx5MlSpVMpVVqtSDjz7qwZ49UK5crkH6sQO33AJ//AGVK+cRpoiISMkyZcoUpkyZ\nkqls27ZtRRSNiCSaOeeKOoZQItAFaO+c+ymK+iOBzs65ZmFlLwP7Rw5IDtveHJg/f/58mjdvnmnb\nV1/B8cf7cQPHHZfHwX/+GerVg9dfhwsvzCtUERGREm/BggW0aNECoIVzbkFRxyMi8VPk3YTMbCxw\nCX6K0O1mdnDwlRxW5z4zC19D4Cmgnpk9YGYNzOxa4CIgpkUAmjeHChWi7CpUty40bgzvvBPLoURE\nREREio0iTwaAa4DKwGzgl7BXt7A6NYFaoTfOuZXAWfj1BRbhpxTt65yLnGEoKmXL+icDUY8LPucc\neO89Pw2RiIiIiEgJVeTJgHMu4JxLyuY1OazOFc65DhH7zXHOtXDOpTjn6jvnXihIHO3aweefQ3p6\nFJXPPht++w3++9+CHFJEREREpEgVeTJQXLRtC5s3Q1SrNx9/vJ+PVF2FRERERKQEUzIQdPzxkJTk\nnw7kKSkJzjwTZsxIeFwiIiIiIomiZCBov/38TEKjR8OWLVHscPbZ8O23sGpVwmMTEREREUkEJQNh\nnn4aNmyAs86C7dvzqNypE5QpA+++WyixiYiIiIjEm5KBMEcfDe+/D4sXwwUXwO7cli+rUgVOOkld\nhURERESkxFIyEKFVK5g+HT77DC69NI/ZQ885B2bNiuIxgoiIiIhI8aNkIBunnAKvvAJvvgn9+kGO\nizSffbZ/fPBxTMsbiIiIiIgUKSUDOejSxY8hePpp+OSTHCodeSQ0aKApRkVERESkRFIykIvevaFy\nZZg3L5dK55zjBxFHtVqZiIiIiEjxoWQgF4EANGsGixblUunss/0URAsWFFpcIiIiIiLxoGQgD6mp\neSQDJ54I++8PDz2Ux/RDIiIiIiLFi5KBPKSmwvff5zJhUJky8Pjj8PbbfqrR1asLNT4RERERkVgp\nGchDaqqfTWjx4lwqXXYZfP45/PorNG8OH35YaPGJiIiIiMRKyUAejj7a3/zPtasQ+AUK5s+H1q2h\nc2e4995c5iQVERERESl6SgbykJwMjRpFkQwAVKvmVyQeOhSGDYNp0xIen4iIiIhIrJQMRCHPQcTh\nAgG4+27/pGDChITGJSIiIiJSEEoGopCaCt9+C2lp+djpqqvggw80oFhEREREii0lA1FITYWdO2HF\ninzs1L07VKwIzz6bsLhERERERApCyUAUmjXzP6PuKgSw337Qowc891w+HymIiIiIiBQOJQNRqFYN\natXKZzIAvqvQ2rW+u5CIiIiISDGjZCBK+RpEHNKihd9RA4lFREREpBhSMhClmJIBM7jySnj3Xfjl\nl4TEJSIiIiISKyUDUUpN9QsMb9iQzx0vuQTKlYPnn09IXCIiIiIisVIyEKXUVP8z308HqlSBiy+G\nZ56B9PS4xyUiIiIiEislA1GqUwcqV44hGQDfVWjlSvj44zhHJSIiIiISOyUDUQoE/BSj33wTw84n\nnACNG2sgsYiIiIgUK0oG8iGmQcTgBxL36wdvvQU//BD3uEREREREYqFkIB9SU2H5cti+PYad+/SB\ngw6CkSPjHpeIiIiISCyUDORDs2bgHPzvfzHsnJICN90EkybBqlVxj01EREREJL+UDORD48aQlBRj\nVyGAa67xsws9+GBc4xIRERERiYWSgXxIToZGjQqQDOy3HwweDM8+q0XIRERERKTIKRnIp5gHEYcM\nGOC7DD30UNxiEhERERGJhZKBfGrZEhYuhB07YmygShUYNAjGj4eNG+Mam4iIiIhIfigZyKfTT4fd\nu+GzzwrQyHXX+cEHo0bFLS4RERERkfxSMpBPDRvC4YfDBx8UoJGqVaF/f3jySdi8OW6xiYiIiIjk\nh5KBfDKDzp3h/fcL2NANN0BaGlx/PaSnxyU2EREREZH8UDIQg86dYcUK+PHHAjRSvTo8/TS8+CJc\neaUSAhEREREpdEoGYtChA5QtW8CuQgCXXAKTJ8PzzyshEBEREZFCp2QgBpUqQdu2cegqBNCrl08I\nJk5UQiAiIiIihapMUQdQUnXuDEOGwK5dfjGyAunVy//s3dv/fOYZPzhBRERERCSB9GQgRmec4dca\n+Pe/49Rgr17+6cBzz8G0aXFqVEREREQkZ0oGYtSkCRx6aJy6CoVceqnvf/Tww3FsVEREREQke0oG\nYhSaYrTAg4gj3XwzfPEF/Oc/cW5YRERERCQzJQMFcMYZsHQprFqVuXzlSrjqKtiyJYZGzz4bjjoK\nHnkkHiGKiIiIiORIyUABdOwISUmZnw6sXg2nnOKXEPjwwxgaDQTgxhvhzTfhhx/iFquIiIiISCQl\nAwVQpQq0afPPuIG1a30iYAY1a8LXX8fY8GWXwUEHwaOPxi1WEREREZFISgYKqHNn+OQT31WoQwf4\n+2+YNQvatYP//jfGRpOTYcAAvxjZpk1xjVdEREREJETJQAF17gx//QUtWsDOnfDpp1CnDrRuDQsW\n+OQgJv36+Z9jx8YrVBERERGRTJQMFFCzZnDIIVCunH8iUK+eL2/Vyq9DsGRJjA0feCD06QNjxvgs\nQ0REREQkzpQMFJAZzJwJ8+ZB/fr/lDdv7scCx9xVCGDwYN9N6IUXChyniIiIiEgkJQNx0LixfzoQ\nbr/94OijC5gMHHEEXHABjBoFzhUoRhERERGRSMUiGTCzdmY23czWmVm6mZ2bR/32wXrhrzQzq15Y\nMUejVasCzCgUMnAgLF8On30Wl5hEREREREKKRTIAVAQWAdcC0d4Cd0B9oEbwVdM5tzEx4cWmVStY\nvBh27SpAIyedBA0bwrhxcYtLRERERASKSTLgnPvAOXePc24aYPnY9Tfn3MbQK1Hxxap1az+b0KJF\nBWjEDK65xi9C9uuvcYtNRERERKRYJAMxMmCRmf1iZjPNrE1RBxSpaVM/y1CBuwpddhmUKQPPPReX\nuEREREREoOQmA+uBq4ELgQuANcBsM0st0qgilCsHqakFHEQMcMAB0L07TJgAaWlxiU1EREREpEQm\nA865751zTzvnFjrn5jrn+gJfAoOLOrZIrVvHIRkAvwjZypXw4YdxaExEREREBMoUdQBx9DVwYl6V\nBg8eTJUqVTKV9ejRgx49eiQkqFat/LphW7fC/vsXsKFjj4WnnoIzz4xbfCIiIuGmTJnClClTMpVt\n27atiKIRkUQzV8zmrzezdOA859z0fO43E/jDOXdRDtubA/Pnz59P8+bN4xBpdJYu9esNfPwxnHpq\nARubMME/Ifj5Zzj88LjEJyIikpcFCxbQokULgBbOuQVFHY+IxE+x6CZkZhXNrFlYn/96wfe1gtvv\nN7NJYfWvM7NzzewIM2tsZo8BpwBjiiD8XDVoAJUqxamrUI8eULEiPPNMHBoTERERkdKuWCQDQEtg\nITAfv37AI8ACYGhwew2gVlj9csE63wKzgabAqc652YUTbvQCAWjZMg4zCoHPKnr18snA3r1xaFBE\nRERESrNikQw45z5zzgWcc0kRrz7B7Vc45zqE1X/IOVffOVfROXeQc+5U59ycovsEuWvVKk5PBsCv\nObB+PUybFqcGRURERKS0KhbJwL6udWtYu9ZfwxfYMcdAmzYwdmwcGhMRERGR0kzJQCFo1cr/jNvT\ngQED4NNPYcmSODUoIiIiIqWRkoFCUKsWVK+edzKwdSsMGQK7d+fR4IUXwsEHw5NPxitEERERESmF\nlAwUArPoFh8bOtS/vvwyjwbLlYMrr4TJk+GPP+IWp4iIiIiULkoGCskpp8CsWbB4cfbbv//eL04G\nOdfJ5OqrYedOeOGFuMUoIiIiIqWLkoFC0r8/1K8PV1wBf/+ddfstt8Ahh/gFyqJKBg47DM47z3cV\nKmYLx4mIiIhIyaBkoJCULw/PPw8LF8LDD2fe9umnfqbQBx7waxJ8+22UjQ4Y4Jc4/vTTuMcrIiIi\nIvs+JQOFqHVruPFGuPdefw0PkJYGN9wAxx8PF18MTZvCd99BenoUDbZvD40bayCxiIiIiMREyUAh\nGzoU6tSBPn18IjB5MixaBKNG+YHGTZvC9u3w889RNGYG114Lb78Na9YkOnQRERER2ccoGShkKSm+\nu9BXX8GIEXDnndC9O5xwgt/etKn/GdW4AYBLL4WKFWH8+ITEKyIiIiL7LiUDRaBNG7juOt9daPNm\nuP/+f7bVrAnVquUjGahUCXr3hqefjmKBAhERERGRfygZKCIjRkDz5j4hqFPnn/JQV6GoBxEDXHUV\nbNwIH30U7zBFREREZB9WpqgDKK0qVIB58/zFf6SmTWHmzHw01qQJNGwIr70GZ58dtxhFREREZN+m\nJwNFKLtEAHwysGKFX1Ms6oa6dvXzk6qrkIiIiIhEKaZkwMzOMLO2Ye/7m9kiM3vZzA6IX3ilU9Om\nfmrR0PSjUenaFbZtg08+SVhcIiIiIrJvifXJwENAZQAzawo8ArwH1AVGxSe00qtJE/8z6kHEoZ0a\nNPBdhUREREREohBrMlAXWBL884XADOfcHUB/oHM8AivN9tsP6tXL5yDiUFeht9+GPXsSFpuIiIiI\n7DtiTQb2ABWCf+4IhIa7bib4xEAKpmnTfD4ZAJ8MbN2qrkIiIiIiEpVYk4HPgVFmdjfQGng3WH4U\nsDYegZV2MSUDTZvCUUepq5CIiIiIRCXWZGAA8DdwEdDPObcuWN4Z+CAegZV2TZvChg2waVM+dgrv\nKrR3b8JiExEREZF9Q0zJgHNutXPubOdcM+fcs2Hlg51zg+IXXul1zDH+Z0xdhbZsUVchEREREclT\nrFOLNg/OIhR638XM3jaz+8ysXPzCK72OPBLKl8/nIGLwWUT9+uoqJCIiIiJ5irWb0Hj8+ADMrB4w\nFdgBdAUejE9opVuZMnD00TE8GVBXIRERERGJUqzJwFHAouCfuwJznHM9gcvxU41KHMQ0iBh8MrB5\nM8yaFfeYRERERGTfEWsyYGH7dsQvOAawBjiwoEGJ17QpfPedX404X5o18/2M1FVIRERERHIRazIw\nD7jLzC4F2vPP1KJ1gV/jEZj47v/bt8PPP+dzRzPo1g3efFMLkImIiIhIjmJNBq4HmgNjgBHOuR+C\n5RcBX8YjMPFPBiDGrkI9e/pZhT7QTK8iIiIikr1Ypxb91jnX1DlXxTk3NGzTzUDv+IQmNWpAtWox\nzCgE0Lixf7Tw8stxj0tERERE9g1lCrKzmbUAGgXfLnHOLSh4SBJiVoBBxOCfDgwdCn/+CZUqxTU2\nERERESn5Yl1noLqZfQr8F3gi+JpnZp+Y2UHxDLC0a9kSZs/21/P51r077NwJ06bFOywRERER2QfE\nOmZgNLAf0Ng5V9U5VxVoAlTGJwYSJwMH+kRg1KgYdq5dG9q2VVchEREREclWrMnAGcC1zrmloQLn\n3BKgP9A5HoGJd/jhPiF46CH4NZZ5mnr2hJkz4bff4h6biIiIiJRssSYDASC75W33FqBNycHtt0PZ\nsjB8eAw7d+3qBx9ozQERERERiRDrhfss4HEzOyRUYGaHAo8Gt0kcVa3qE4Lx42HFinzufOCB0KmT\nugqJiIiISBaxJgMD8OMDVprZj2b2I/AzUCm4TeJs4EA/1ehdd8Wwc8+e8MUXsHJlvMMSERERkRIs\n1nUG1uAXHTsLeCz4OhPoAtwTt+gkQ0oKDBsGr74K//1vPnc+91yoUAGmTk1IbCIiIiJSMsXcv995\nHznnRgdfHwPVgL7xC0/CXXaZX0vsllvAuXzsuN9+0KWLugqJiIiISCYa7FuCJCXByJF+3YEPP8zn\nzj17+tXLYl7BTERERET2NUoGSpizzoI2bfzCwvl6OnD66VCzJtx0E6SnJyw+ERERESk5lAyUMGZw\n550wdy58+mk+dixXDiZPho8+8o8XRERERKTUK5Ofymb2Zh5V9i9ALBKlzp3h2GNhxAjo0CEfO3bs\n6DOJu++Gdu38S0RERERKrfw+GdiWx2sVMDmeAUpWZnDHHTBrln9CkC/33gtt20KPHrBpU0LiExER\nEZGSIV9PBpxzVyQqEMmfCy6Ahg3904F33snHjmXK+FmFUlP99EQzZkBAvcVERERESiNdBZZQgYBf\nlXjGDPjmm3zufOih8MIL8P778MgjCYlPRERERIo/JQMlWI8eUKcO3HdfDDufcQbceqvvb6TpRkVE\nRERKJSUDJVjZsv56/rXXYPnyGBoYNgzq1YMBA/I5T6mIiIiI7AuUDJRwl18ONWrEOFtouXIwejTM\nmQNTpsQ7NBEREREp5pQMlHDJyXDzzTBxop9lKPzVo0cUDZx+Olx4oV+M7I8/Eh2uiIiIiBQj+ZpN\nSIqn/v2hWjXYvfufstmz4e23Ye9e350oV6NGQaNGvtvQww8nMlQRERERKUaUDOwDypXzs4SGa9rU\nzyC6aBG0apVHA4cf7hcju/deuOIKaNw4YbGKiIiISPGhbkL7qObNfReizz+Pcocbb4S6dTWYWERE\nRKQUKRbJgJm1M7PpZrbOzNLN7Nwo9jnZzOab2S4z+97MehdGrCVFuXLQunU+koHy5eGJJ3z/oqlT\nExmaiIiIiBQTxSIZACoCi4BrgTxvS5tZHWAG8AnQDHgceMbMTktciCVP27Y+GYj6Rv8ZZ8BFF8GV\nV/qkQERERET2acUiGXDOfeCcu8c5Nw2wKHbpB/zknLvFObfcOfck8DowOKGBljBt28LGjfDDD/nY\nadIkaNMGOneGmTMTFpuIiIiIFL1ikQzE4Hjg44iyD4ETiiCWYuuEE/wUo1F3FQKoUAGmT4cOHeCc\nc+DddxMWn4iIiIgUrZKaDNQAfo0o+xWobGbliyCeYmn//f2sQvlKBsCPPH7zTTjzTDj/fHjrrYTE\nJyIiIiJFq6QmAxKltm3hiy9i2LF8eXj1VZ8MdO0KX34Z99hEREREpGiV1HUGNgAHR5QdDPzhnNud\nTf0MgwcPpkqVKpnKevToQY+olustedq2hbFj4bff4KCD8rlz2bLw0kvw9dcwZYofSyAiIvu0KVOm\nMGXKlExl27ZtK6JoRCTRzBWzOeXNLB04zzk3PZc6I4HOzrlmYWUvA/s7587MYZ/mwPz58+fTvHnz\neKZ/zVIAACAASURBVIddbK1Z49cUe+stOO+8GBu55hqYNQu+/z6usYmISMmwYMECWrRoAdDCObeg\nqOMRkfgpFt2EzKyimTUzs9RgUb3g+1rB7feb2aSwXZ4K1nnAzBqY2bXARcCoQg692KtVyycD+R43\nEK5TJ1ixAn7+OW5xiYiIiEjRKxbJANASWAjMx68z8AiwABga3F4DqBWq7JxbCZwFdMSvTzAY6Ouc\ni5xhSPhnvYGYdegASUnw4Ydxi0lEREREil6xGDPgnPuMXBIT59wV2ZTNAVokMq59Rdu2fizwjh1+\n5tB8q1LFz1P64Ye+y5CIiIiI7BOKy5MBSaC2beHvv/044Jh16gSffAJ790ZXf/duP0hh+fICHFRE\nREREEknJQCnQuLG/uV/gcQN//glz50ZXf948mDbNr1eQm/POg1tvLUBgIiIiIhIrJQOlQCAAJ55Y\nwGSgeXOoVi36cQP/+Y//+dVXOdfZs8e3N2tWAQITERERkVgpGSgl2rb164alpeVe77vvcpg0KCkJ\nOnaEmTOjO2AoGZg7F3KavnbRIti1CxYvjr77kYiIiIjEjZKBUqJtW9/L55tvsm77+2944w1o3x6a\nNIFGjeC557JppFMn3/1n06bcD+acTwYaN4Zff4XVq7OvF0oYdu/W2AIRERGRIqBkoJRo1QpSUqB1\na3+xf9FFcM89MHw41Kvn3zsHr7wCvXtD375w1VX+xn2G00/3lT7OYwbX1ath/Xq47jr/PqdxBl9+\n6RMG8E8JRERERKRQKRkoJZKTfff9J5+E006DLVvg6adhxAjf+2fBApgzB7p1g/Hj+f/27js8iurr\nA/j3JnQUFZGmIkpXEWkCIgICIoqAIkVRRF4piiKI2BH1RxOwADYURCyggqKi0hUpCUW6CgjSCTW0\nkBCS3T3vH2cn2b6bZLMbk+/neeaBnZ2dvTvZwD1zz7kXU6cCn32mIwp79zpPcvnlOnQQrG7AuuPf\nsSNw9dX+6wbi44F27TQaYTBAREREFHF5Yp0BiozatXVz5XBogbGn3r2BG28EOnfW2uHly4Frr4Wm\nCs2cqSMExvh+o/h47eCXLQs0auR7ZODAAWD/fl2/YNcuYMOGHH8+IiIiIsoajgwUcL4CAUu9esC6\ndVo7/OWXzp1t2wIJCcCff/p/4apV2skHgMaNddghLc39GGv0oEkTjTo2bvRfaExEREREuYLBAAVU\nujTQsiWwdKlzR7NmmnPkL1UoNVXv8lvBQKNGWiDsWbkcFwdUrgxUqKDBwIkTOlpARERERBHDYICC\natFCVy8+exYaCDRv7n+K0XXrdJpQKxioWxcoUsS7biA+Hrj5Zv37jTfqn6wbICIiIoooBgMUVIsW\nOv1oXJxzxx13aLXxiRPeB8fHAyVKADfcoI+LFtXOvmvdQGqqpg5ZAcMVV+iCZqwbICIiIoooBgMU\nVM2aQLlyLqlCDzygf37wgffB8fE6j2khl9r0xo3dRwas0QNrZMCYzLoBIiIiIooYBgMUlDE6OpAR\nDJQtC/TqBUya5L4QgbXYmHXH39KoEbBzZ+ZiZXFx7qMHAIMBIiIioihgMEAhadECWLvWWTcAAEOG\nAEePAp9/nnmQtdhY48buL7Yer1mjf8bH6+pnrqMHN94I7N4NnDqVWx+BiIiIiDwwGKCQWHUDK1c6\nd1SrBtxzDzB+vC5WAGTWBXiODFx9NVCmjD5vjR5YKUIWq4h48+bc+ghERERE5IHBAIWkRg2gfHmX\nVCEAGDoU+Ocf4Mcf9bHrYmOujMmsG9izBzh82DtgqFlTi41ZRExEREQUMQwGKCRedQOAdvCbNQPG\njdPHvuoFLI0aaTBgDS14phIVKqTLI7NugIiIiChiGAxQyLzqBgAdHYiLA5YscV9szFPjxsDp08Cn\nnwLVq2vakCcWERMRERFFFIMBClmLFoDd7lI3AAB33QXUqgX06eO+2Jinhg11eGHJEu96AcuNNwJ/\n/QWkpYW76URERETkA4MBCln16lo38NtvLjtjYoBnnsH03c3wU9F73acLdXXRRRo0AP4Dhrp1NaD4\n+++wtpuIiIiIfGMwQCEzBmjZ0qNuAMCM2IfQC9MxrPAY9+lCPTVqpH/6GxmoXVvfhKlCRERERBHB\nYICypEUL4I8/gKQkfRwXB/TuVxhXV0zFpuSqOHkywIvbt9fRgWuv9f38hRcCVasyGCAiIiKKEAYD\nlCWudQO7dwOdOun6YfN+LQYRg+XLA7z43ns1BSgmwNeORcREREREEcNggLKkWjWgQgXghx/0Rv+F\nFwLffaf1BJUqAb//nsM3sIIBkbC0l4iIiIj8YzBAWWLVDXz4IXDwIPDzzzpLqDFA8+be9QRZVreu\nTkG6Z08YWktEREREgTAYoCy7806tE549WxcOtrRooTf1T53Kwcnr1tU/167NSROJiIiIKAQMBijL\nHngAOHwYaN3afX/z5oDDAaxYkYOTly8PVKmCwMUHRERERBQODAYoy4wBLr3Ue/811wBXXBGGuoFm\nzRgMEBEREUUAgwEKm7DVDTRrBmzenLV8IxFg504WHhMRERFlAYMBCqsWLYD164EzZ3JwkmbNtFMf\nFxf6a6ZM0amOpk/PwRsTERERFSwMBiiswlI3ULUqUK5c6KlCW7cCTz0FlCoFvPBC5opoRERERBQQ\ngwEKq6pVgYoVc1g3YEzodQOpqcD99wOVKwOrVmlq0ejROXhzIiIiooKDwQCFVVjrBtau1c5+IM89\npyMDM2cCtWoBQ4cCb74J7NqVwwYQERER5X8MBijsmjcH1q3LYbZOs2ZAWhqwZo3/Y37+GZg4ERg3\nDqhTR/c99xxw2WUaFBARERFRQAwGKOxatADsdmDlyhyc5IYbtAbAX6rQoUNAr17AXXcBTz6Zub9k\nSWDMGOC778IwPEFERESUvzEYoLCrXl3rf3NUNxAbC9x8s/9g4NFHdRnkadM0N8nVAw8AjRsDgwZp\nVEJEREREPjEYoLAzRkcHwlI3EBeHJQvt6NPHZf/SpcAvvwDvvqspQZ5iYoAJE4BNm4CpU3PYiP+Y\nPXu41gIRERGFjMEA5YrmzYE//gDOns3BSZo1A5KS8NnEU5gyBdiyBdrRfflloF494N57/b/2ppuA\nHj2AUaNy0IAAUlM10DhwIHfOnx2HD+taC99+G+2WEBER0X8EgwHKFS1aADYbMHKk/pktDRsCRYog\nfo1+TWfOBDB/vhYjjBjhnR7k6b77gL17gX37stmAAL79VlOVKlcGOncGFi/WBRaiafVqvdhLlkS3\nHURERPSfwWCAckXNmsDw4cDYsZr6v21bNk5SrBiO122DHccuQfnywFdfCeSll4GmTYE77gj++ptv\n1j8DVTKfOqUFyDt2ZK1tK1fqogoTJwLbtwNt2ujUpj/8kLXzhJM189KyZdFrAxEREf2nMBigXGEM\n8OqrQFwccPo0ULcu8PbbWb95vurKLgCAUSMFu3cbrN5QWIcbgo0KAEDZspo2EygY+P57rT1o0CBr\nHfm4OODWW4HHH9f8pWXL9P2eeCJ6OfurVwPFigF//w0cOxadNhAREdF/CoMBylWNGgEbNgD9+wNP\nP6039LMSEMTHNkU5HEbPRttRodBRzLzyOS1ICFXTpoGDgcWLgeuvB1q1Ajp1Al58MfgMRGfOaABg\njTxYKya/+KLWEPzzT+jtCxeHQxdp69VLH4eyejMREREVeAwGKNeVKKGjAj/8ACxaBPz0U+ivjU+o\njCaIR+xTT6Cb7Ut8k3JX1mYLbdoU2LzZ9wpoIppff+edWgMwdizwxhtA27aB76yvXq2d76ZN3fff\neitQuLAGGJH2zz8apHTuDFx9NVOFiIiIKCQMBihiOnTQ/vO4caEdb7MBa9YXQpMKe4ElS3B/s4M4\nnFgka1OWNm2qHfdVq7yf27pVZ+Bp1Urv7g8dqh35LVuAu+/2f86VK4HSpXVBBVclS+poQTSCAate\noEEDDUpyIxiYPl3To4iIiCjfYDBAETV0KLBiBRAfH/zYLVuA5GSgSRMAxqDhpJ6oUsU5q1CoatTQ\njruvVKElS4AiRYBbbsnc17Kl1hCsXg3s3u37nHFx2umP8fHr07o18NtvOZhCKZtWr9aq7Ysv1mBg\n40Yt1giXtDRgwAAdPSEiIqJ8g8EARdTdd2v/PJTRgfh4XWS4wah7ga+/hqlzA7p314ye8+dDfMOY\nGO24+woGFi/W50qUcN9/xx0aJPgqKLbbdZTBqhfw1Lq1dsL/+CPEBobJmjW6tgKgwYBI4FqJrFq5\nUiOzuDguakZERJSPMBigiIqJAZ55RifxCVZnGx8P3HgjULxGJaCLzip0//06G+iCBVl406ZNtQPv\nerfeZtOVjFu18j7+wgu1U//9997P/fmn1h941gtYGjQASpWKbKpQaqqutmwFA1WqABUqhDdVaP58\n/fPYMWDnzvCdl4iIiKKKwQBF3IMPAuXKAW++Gfi4+HhnipCL664DatfOYqpQ06a6FPKWLZn71q3T\ngltfwQAAdOyoM/IkJrrvX7nSOVzRwPfrChXSVKNIBgMbNwLp6ZnBgDHhrxuYP1+viTHhHXEgIiKi\nqGIwQBFXrBgwcKDWox4+7PuYo0eBf//1DgYAHR348UfNWglJgwY6y49rJ3bxYh0BaNjQ92s6dNB0\nGM+pj+LigHr1vFOLXLVurceF3MAcWrNG05rq1Mncd+utOtVoSkrOz5+QoDMyde+u0RiLiInCZ9Ei\n//8QEhFFAIMBior+/bV/PmmS7+etyX98peZ376593KlTQ0xfL15cO/CuwcCSJUCLFnon35fy5YHG\njb1ThVau9F8vYGndWu/UR2qu/zVrdFW3IkUy9zVvrqlQvmZRyqoFC3REoE0b//UXRJR1IsA99/j/\nh5CIKALyTDBgjBlgjNltjDlnjFlljPFzyxYwxjQ3xjg8Nrsxpmwk20zZd8klQJ8+wPvvawaPp/h4\nTXuvVMn7uauv1oyVp57S2T3feAM4ckSfs9v1tS+9pPUGLVo4AwbXxcfOndO72/5ShCydOmlH2Lq7\nnpAA7Nnjv17AUqMGcPnlkUsVci0ettSqBVx6aXhShebN0/Nfeql+9r//Bk6ezPl5iQq648d1BHHr\n1mi3hIgKsDwRDBhjugF4E8BwAHUBbAKwwBhTJsDLBEA1AOWdWwUROZrbbaXwGTRIA4EPPvB+zqoX\nMMb3a+fM0frfRo2A4cOBK64AbrtNb+jffDMwebL2x3//XTc0bQrs36/bypU6HVGwYKBjRw0crE69\nlR4TbGTAGB0diEQwcOIEsGOHXghXMTG6KnJOgwGbTdMY7rhDH1uBUChzwxJRYHv36p8MBogoivJE\nMABgMIDJIvKZiGwD0B9ACoDeQV53TESOWluut5LCqlIlTRd6+WX3vqXNpunuvuoFLMZoJswXX+gN\n+7feAooWBfr21b7+kSOa7l+rlo4+ZHRiV67UTnq5cpr/HkiNGjp3v5UqFBcHVK4MVKwY/MO1bq0z\n/BzN5a/l2rX6p+fIAKB1A/HxukZAdq1Zo9M3WcHANdcAZcsyVYgoHKxgYOdOTS0kIoqCqAcDxpjC\nAOoDWGLtExEBsBhAgO4gDICNxpgEY8xCY0yQ27WUF40fr/W9994LHDyo+zZv1sycQMGAq9KlgSef\n1GyWkSP1xn1srAYMjz+uowgJ9nI65ebKlVovYK06HEynTsDcuZp/FEq9gMUadfj119COt4gAs2fr\nlElLlgQ/fs0aXWisalXv5269VacdzcmaB/Pn6wW2Cq2N0cCKRcT50/Hj7JRGkhUM2GycspeIoibq\nwQCAMgBiARzx2H8Emv7jyyEA/QB0BnAvgP0AlhpjbsytRlLuKFoU+O47rePt1EmzcuLjtbi4fv2c\nn/+hh/Q9Pv4Y2omdN0+nFQ2WImTp2FE7SEuWAOvXB68XsFSooCMPWUkV2rNHV2Xr0gXYtQsYMyb4\na1av1lEBX4FNnTo6Y1JOUoXmzwduv12jK0vTpvq+4ew0vvaaRn92e/jOSVljs+l39t13o92SgmPv\nXqCMMxt227botoWICqy8EAxkmYj8IyIfi8gGEVklIv8HIA6abkT/MeXK6WK/f/2lRcVxcTo5TrFi\nOT/3RRfpugYffQSkN75F5ysV0TSeUNx0kxYivPyydpZCHRkAgDZtkDh/LcQRZMojm02HSK67TtcM\nmDNHix4WLwa2b/f/OhEdGfCsF7AUKqQd9+wGA8eO6aiClSJkuflmjdo2bszeeT05HMCUKTrz0Wef\nheeclHWrV2ta24YN0W5JwbF3r466XXwx6waIKGryQjBwHIAdQDmP/eUAZGXy5TUAfORKuBs8eDA6\ndOjgts3M0gpWlBvq1QOmTQO+/BL4+uvQU4RC8dhjWlfw4/m2uqNq1YxpipKS9H39ptXHxOjowNq1\nwAUXaPpOiA7XvwuVD67AsCeCzLzz6KPAc89pJLR1qw6RdOmidwx9VVdb9u7VDruvegFL8+Zaaf3n\nnyG3O8OiRRpw3H67+/569XS4JVypQqtWAQcOaIHHyy+HZ22EYLZs0REiymQt68071JGzdy9w1VX6\n3c9DwcDMmTO9/p8cPJj32ojyLRGJ+gZgFYAJLo8NNPVnaBbOsRDA7ADP1wMg69atE8q7XnpJBBD5\n+uvwnrdpU5GWLR0ipUuL9O8vIiJJSSK33KLvN3FigBf/8ose1Lp1lt5z6FPnBRApVihN9u/3c9D+\n/SKxsSJvv+393PPPi1x0kcjZs75f+/XX2q4jR/w34tQpkTp1RCpUEPn33yy1Xx58UOTGG30/d8st\nIl26ZO18/jz1lLZvxw6RwoVFRowIz3kDufVWkcqVRex2ERE5d06kc2eRFSty/63zrIYNRYwRufBC\nEYcj2q0pGC65RGT0aJHevUUaNIh2awJat26dQGfxqyd5oN/AjRu38G15YWQAAN4C0McY09MYUxPA\nhwBKAPgUAIwxo40x062DjTFPGWM6GGOqGGOuM8a8A6AlACa7/se9/jrwyy9A587hPe+AAcBvvxls\nnbwMeP11JCcD7dvrhD8tWwIjRvhe7wCAzll6ySW6aEGITpwAPphaBI9XX4ySjiQMH+bwfeCHH+qi\naL19TJzVrx9w5gwwY4bv165erbMblQ2wvMZFF+kd35IlddGwQ4dC+wAOh77OM0XIYi0+JqGs+hbk\nfWbNAu67T0dsBgzQhSNycxam8+f12u3Zo6Mf0Oykb7/V2ahsttx76zzr+HFNCevYUYfLQv2eUPad\nOaPrdVx1lc5atm1bzn+fiIiyIU8EAyLyDYBnALwOYAOAGwC0FZFjzkPKA7jS5SVFoOsSbAawFEBt\nAK1EZGmEmky5JCYGaNfOvV41HO69V/vMHyy7DiklL0OHDpolMn++pgmdOgW8846fFxctqlHDkCEh\nv9+kSVoLO/yjy/GK41V8Ol1rItykpmoxwyOPAKVKZexOSdF+8Y+bK2vE8v773p2EvXuBzz/XNKBg\nypXTTu/580Dbtl4Lhp04obMxufW/N2zQFCR/wUDTppp7tW9f8PcPJC5Oz9O1qz5++WX9Erz2Ws7O\nG8jatXotSpcGJk+G3Q6MG6cF61u3OovNC5rFi/U7NnCgPg5Uq0LhYc0kZKUJnT2r6XJERJEW7aGJ\nSG1gmlCB9+KLIqVKabZPiRIiy5ZlPjdokD53/Lj365KTRXr2FPn++9De58wZHf0fOFAfn7+zk1xT\neK/c3d4j9WL6dBFAZPt2t93ffae7GzYUcfwyTx+sXJl5QFKSyA03aJrL0aOhNUpE5K+/NE2qSZOM\n1KPkZJGbb9a3+OILl2MffVQvyPnzvs917Ji+6MsvQ39/X558UuTyyzPSdUREZOxYTZ3ati1n5/Zn\n1ChNhZkwQSQ2Vr75MFEAkbVrRXr1Ern0UpGTJ3PnrbMkMVHTmZ59Nvff6+GHRWrXFklLEylUSOT9\n93P/PQu6uXP1d+jAAZGdO/XvCxZEu1V+MU2IG7f8u0W9ARH7oAwGCry9e0ViYkSKFxf57Tf3544e\nFbngApFnnnHfn5Ymcued+ptSvLjIpk3B32fcOE1937fPuWPFCpmJbgK4BCAOh+YIt23r9foHH9Rg\nBRBZudwucs01Ij166JN2u0iHDtqZ3bIlKx9frV6tH7RdO0lLTpO77hIpWVJLE1580XnMrFn65pMn\nBz5X9eoijz+e9TZYbDatFRg0yH3/uXMiV10l0qlT9s8dSLt2InfcIXLqlDiKFZd6FRMyykEOHtTr\nMWRI7rx1yBITRerWzfwiTJ2ae+/lcOjPwfry16yZGclS7nn3Xf2Hwm7X34WiRTVAzaMYDHDjln+3\nqDcgYh+UwQCJyCef+C8SHT5cpFgxySj2tdu1Y164sI4K1Kmj/fITJ/yf/9w5kfLlRf7v/9z322++\nReqX/FsaN3ZobWZ8vP76/fST23Hnz2vHfNgw7Wvfd59odFGkiBYKP/ecFnl6vC5LFi4Ue2xh6Vl1\nhRQu7JAFC0TatHH2vXft0gZ06RK8iPSRR/wXGIfi99/1GsTFeT/35Zf63PLl2T+/LzabjniMHCki\nIovajhNAZNF8W8Yh//uf/sz/+Se8bx2yxESRevV0iGLTJpE+ffTnv2pV7rzfpk16rRcv1sedOonc\nfnvuvBdlGjpU/0Gx1K6dMblBXsRggBu3/LtFvQER+6AMBiiI06e1/9Wnj/aDBw3SfvdXX+nz//6r\n6T/t2mmf0pf339fRB6+O5Ny5sgQtBRCZPVtE7r9fpEoV9/QYEZnnzAravFnkvff0XHs2nNAo5dZb\n9cnx43P8WZ9p96cY2GVG59kiohP61KjuEGnUSNOPQsmTmTJFG3j6dPYaMWCAyJVXel0DEdF91at7\nR1WBnDol8uGHGtm45oC5Wr9eXIdoWjU4JfWxVtOxnFJStFm5NTAR0IkT7oGAiEhqqqZ2VawokpAQ\n/vd84w0dgUhN1cfPPy9SqVL434fcde0q0rKl++PmzaPWnGAYDHDjln+3qDcgYh+UwQCF4K23NF39\n8cf1t+O999yfnz9fA4Rhw7xfm5am2S3du/s4sd0ucv31csdla6XaNemSFltM38xDnz4iVatqMJKU\nJHLxxc6UlV69tEG9e+do2sfTp3VwARCZ2H6B/mXKFPnwQ5FYY5PU2BKh34HetUtvodeurelHWWGz\niZQrJ/L00/6PefpprScI9HkdDpGlS0UeekjzuGJi9M5/hw6+j58wQdMxzp2TNWv048+q9LRXz3/G\nDH3u11+DfI6UFJ1mNeiBITh5UqR+fQ0ENm50fy4hQVN5mjTJ7LSHy223aS6c5dNP9cP7m9KWwqNR\nI/29tgwfLlK2bNSaEwyDAW7c8u8W9QZE7IMyGKAQnDund4UB/b/Zl5Ej9fnvv9f+2/r1WvT7xBO6\n329dwWefyUbcoP3vIv297r7bbCKXXaaddcuzz2rWzpk/94q88or/gt4gEhL0vKVKaf995EjRjnT/\n/iKxsbLswckCiGx5+pOsnXjDBr2TbYwOpSQlhfa6337TixUo8Fi0KMgFFc1zBzSCGjVKk/4nTNAi\n2GPHvI/v3FmkWbOMv1arJmKb9L5GgAcPZhzmcIg0bqypYQFjr6VL9f3Ll89aMbcvzz+vPyDPQMAS\nH6/pQn37Bj/X5Mkif/wR/LikJD2n60IbVgrbhg2htZuyp3x5939kZs7U656YGLUmBcJggBu3/LtF\nvQER+6AMBihEv/6qN+39dQIdDpF77tHfHtetRAmdHMevtDSRSpWkC76Wqy447tWvt/qVrjfZ9+3T\nfmrARdH8tHHPHpEff9SJgYoU0ZrjoUN18pIM6ekid98tx1FaAJFvvvKRshNMerrWNRQvrkMjS5YE\nf81jj2kqSqCedmqqVvOOGeP7+bQ0nR1p4EC38xz966jYYwtrgaYrh0PvvL74omzfrvHLRx+JpheV\nKKHFAi7mz5egsYiMHq1tLFNG5K67crZY13XXud8p9mXKFG3U0qX+jzl+XL80XbsGf09rRhvXGa1O\nnNB9M2eG1m7KunPn9Bp/4hJ8b9woXjOH5SEMBrhxy79b1BsQsQ/KYIDCKClJb75+9ZV23o8cCbEf\nOGmS/IVrxRiH1+yNTz4pcsUV3in03bppeYG/OgXL2bPa2b/lFr3BbAUpFStqf9pvGUByssjo0XJZ\nGbu8+moIn8GfnTu1ruGCCwJXWaena6fcc+omX+6+238etVVg4XIH+/Rp/ewvVf1K0zBcbd+ux8+b\nJw8/rFk3GRk3vXtrcOJykYPFIiKi6Ui33aYF3UGXsg5g9259/axZgY9zOESuvlrzyfz5+GM916WX\n+q7HcPXEE3o+zy9vuXL+h8byohMnRBYujHYrQvfPP/ozcg2cU1I0Qp0yJXzv07Nn1upuAmAwwI1b\n/t2i3oCIfVAGA5QX2O0iW7dKjx6aDn/uXObuyy/3PaOjlbXxww/+T5uYqOnkJUtqbfLo0SI//6wj\nC6HerG7ePLSbyQEdPqw5+SNG+D/GWkhhzZrg53v/fU358VWk3KuXFhm7fMBp0/TURQrZ5B9UdV+r\n4OOPRWJiZPn8swKIfPCBy7lWrRJfszvdfbfGNz45HJrX9dJL+njgQP3socw/6+ndd/1/Tk8vvKCV\n7P5Sxtq00XYBwVOFqlXzPYNN8+Yahf5XvPKKft7ff492S0JjpcDt3Om+/5prwjev7bJlEnJQGAIG\nA9y45d8tT6xATFRgxMQANWti+HDg8GFg8mTdvXYtcPCgrpTsqXFj3caO1dWJPR08qAsR//MP8Ntv\nwIwZwPPPA3feCVx5JWBMaE2rVUtX4M2RcuV0ReUJE4Bz57yft9t1leFWrYCGDYOfr107wGbTFXJd\nnT8PzJmjKxe7fMAvvgCaNAEqXm4wMPZ9yOdfZL5m+XKk12mA/kNKolEjoG9fl/PddJO25803vd5+\n5Urg9Gkfbdu1S1dpbtJEH7/xBlCzJtC9u+8fVCA//wzceqvbStR+3X+/riK9cKH3c8eOAb/+5EeA\nKQAAIABJREFUCrzyClCypK487c+uXcCOHboqtaeaNYFt23y/zm7Xn0lO2e264rG/98mKJUv0z759\n9buR1+3dq9/bK6903x/oumeFwwEMHgxccgmQmMgVpYkoIAYDRFFQrRrQsycwerT2G7/7DrjsMuCW\nW3wfP2wYEB8PVKqk/bwjR3T/jh36mlOngBUrQutf+3Pttdpn8NfPczh0C+qZZ7QDMm2a93Nffgn8\n/TcwalRojapcWTtI8+a571+0SHvo3bpl7Dp4UPvBjz4KvDMhBvPtbfDjx0cyG718Od4u8RK2bgU+\n/FDjsgzGAM8+q9HU2rUZu9u10z6rZywCQH8ggEZqAFCsGDBzJrBnD/DkkyFeLADJydrw9u1DO752\nbeC66/S9PM2Zo39266YRYqBgYMECoFAh4LbbvJ+rWVO/DL4+w5NPaqSZU7NmAZMmaZSbE2fPAqtX\nA48/rgHOmDE5b1tu27sXqFABKFLEfX9YInLo79m6dXpnIDZW/3EgIvIn2kMTkdrANCHKY3bt0syQ\nsWO1JiBQGrh1/MCBmgpUtKhmyZQtqwvGZqx2nAOLF4sA/hfbatZM62wbNBB5+GGtGZ47V7MRVq/W\n7Jjt20XOnBFNMbn6aq0PsJw/r2sY3Htv1ho2eLD3FKMPPihy7bVuh40dq8sxnDqlh95xU6JUxi5J\nWbRCZP9+2Y2rpHiRdBk82M/72Gw6K1GXLm67a9Xyk3b9+OOapuTpk0/0QrZq5VGt7ccPPwS+8L6M\nGKFfhORk9/233aZpQiIib7+tleOex1hattTjffnlF23T7t3u+1NTtSjDGE0Jyy7nVLtStKh+qbK7\nVoWI5sMBmhL20kv6mbduzf75IqFnT83r8zRlil7blJTsn/vsWf19sb7H9evr1Ls5xDQhbtzy7xb1\nBkTsgzIYoDyoXz/tCznrWkNy4oTWBFSooDWyvmbQzI6DByVjylRPhw/rcw89pLW2N92kfVHPGZUA\njQFsa52Le82YkXmSSZN0HYC//85awxYuFAFk2w/bpHlzkYRd53RqJI9q5xtucK952L7VLoVxXl6t\n94M4vpwh7fGjXF7BpsGKPx9+qG3csSNj19NPaxG2V+1F3boaFfmyeLF2yC65JHhRcN++mrufFTt3\n6vW1VsQT0R9STIzWRoiI/PmnHjN/vvfr//1Xn/v8c9/n37XL92t//DHzB52TQtfvv89sf0yMXvfs\ncl2PIiVFA7pmzULPk8/JDFDZ1by57wVJVq6U4FNYBfHaaxoQ/fuvPn7qKf2lzCEGA9y45d8t6g2I\n2AdlMEB50L59+v/2RRdlfQkBmy34DENZ4XBoO0aN8n7uiy/0XwvXm8F2u9743r5d+y6rV2vfMqMO\n9/bbMyfqT0rSYYxgU2f6kpoqUqKEDG2+SgCR+5vt0zdxufu7aZPu+vFH95c+f/NSKYYUeeumGQKI\nfPttkPdKSdF2uhTVWrWebtP/nz2r03cG6sQmJupiBoB+bl9RiMOhHVm/wxUB3HSTSMeOmY/fd66X\ncPx45rkrVvRdkDpsmN7h9zdqYLPpMMs777jv79FD7+jfcotWV2eHw6HDS1Zldvv2+ji76tSRQ12e\nlG++cT5eskSvuRUU+ZOWppFt7druI1iRULmy+4IilsRE8QrysuLAAb27MHRo5r5Zs/ScoYxSBcBg\ngBu3/LtFvQER+6AMBiiPGjfOdwc8Gpo08Z1R8NBDIjfeGPz1DofeMO/YUXTBBuvu8ogRGvXs2ZOt\ndjnuai9XFT0kVaroKRdd7Z5TNXSoTpriGVCdXb9drsA+AUTuunJTaDeBR4zQ9BVn5GNNMTp6tMsx\n1qJpwe7gOhyaNlSypKbkeN6t3rBBz7N4cQgN82ClAVlzxrZoIdK2rfsxPXvqkIkrm01X1gu2eNkN\nN7jPNJSSotPGvv56Zk5WdlYpXuBc+XrBAn1sjRJkZ5Gzo0dFALm3wR4BXAZ0Hn5Yl+/2l8p05owG\nq7Gx4msWqVxls2l+oOfcwpayZbM/rWuvXrrmxalTmfsSEvQzfv119s7pxGCAG7f8u0W9ARH7oAwG\niILq3VtTjF05HDrt/LPPhnaODz7QPtaB/Q6Rhg31DvZFF2m6QjbFPzNbAJFfZydK85hlUu3S4xnT\nstpsenN9wADfr/2+6hCphD2ya3ywYQGnxETtvL/8csauDh08phgdNUpTlXwMzRw/LjJ7tkf2iXW3\nevx494P/9z89T3ZWlj54UPPLp04VOXQo8++urKEa106x1RkPtPqziOZctWiR+diaEnbbtsw1G3zl\nlAXTrJl+L6wLlJ6uOW/+foDHj/tfJOPrr2UlmgigH//55537jx3T6LBaNR0hyFhQQvRa1aunIyNL\nlujo1T33ZP1zeJozx23xtvXrNRbzWnJjn3Nk6+effZ8nu3P8zpqlF8FXkFGlSpAVEYNjMMCNW/7d\not6AiH1QBgNEQY0fr1kGrjewrRScUG9enz7tsqDvt9/qi0uW1JXZsmnQI6ekPBLEdv+D8hdqSaFC\nDnntNX3OKnyOj/fz4kmTxOGrGDbgGw7SfP+kJBHJDHAybrjefbcWCPvQt6+2Z9Agj4BgyBC9k++a\nb9S4saYSiXYes7xmWcuWIq1baz1G4cLePc9Dh7QxX3yRua9bNy2+DjZM8sorIuXLZz7u3l07zpaa\nNUUeeSRr7f39d/EZRLzwggaMnoWz+/drqlOTJj7b6+jTV5oW/0Pq1NFFrcuX1+wfEdEv7j33aAe5\nfHldPW7NGs2fr1gxc1Rn4kS9U5+TguiTJ3U0yVkcP2dOZi3QW295HLt8uT7x55++z9W/v/doTiBn\nzujPAdDvkkvK0+LFzlqkXr10yC4HGAxw45Z/t6g3IGIflMEAUVDWJDKu2TzjxokUL+5+czWY3r1F\nrrpKxHbeprfU3347222yFmR78uLPtHF168oLL2jfa8cO7edUrRqgb5ueroWZWbF3r3YQnTnze/bo\nW8+eLfpGZcq4jRxYTp3STmCTJnp8//4ugVVqqnbyrrtOV5s7ckQ7qtOmydGj2j/NcrbMRx9pAe51\n14nceafvY2rXzix0TkzUgMRzhMKXL7/UBp08qbUFJUuKjByZ+fxzz+niZlkpXLn9dq058EyX2rFD\nvAqak5I0N+2ii8RfNDqnfL+MjKONG8X3YMW2bTpVV5EiekCtWvrztSQm6pdp7NiQP8bChTqh1eLF\nzu/d1KkigDgKF5Gxr6WIMdov79hRYya376ZVgOMMNL288462J5Truny5BjcXXKDpaC5vlJysI3pX\nXSXi+HiKfk98zNr05596eYKVTTAY4MYt/25Rb0DEPiiDAaKgdu/WfxV++SVzX5s2Iu3aZe081oK+\n8+ZJjmdrsRZSXdF1gv5lzBhJTtYazJYtfU4sFB4PPaS59c4i22uv1SAno+PqI81jwgSNIRIStH9o\njAYrGf26LVu0ozdokMinn4oAYk84LO3aaXxxxRXayQxZYqKOCAB6PqfNm12mm3WdbWfSpNDvgq9b\nJxnpRFYRqsssSxIXp/uWL/d+7cGDWsgxYoTIZ5+JLF2qefmAyMyZvt+vZUtNkRHRC9a+vf5wN2/W\n3DXXlCURSd+5R2pgq7Spk/lZGjQQuesuP58nIUFXek5M9H6ue3eRGjVC/q62aKGXEdCXvVPtXTly\nbQvpjakCiLz4osY7VnbYsmUuLx45UqR0af8nt0ZPvvzS/zEOh47cxMSING2aOXOQi/HjJWPipz9m\n7xa3Og0X1kiW2+RQx465/6yFwQA3bvl5i3oDIvZBGQwQBWW3651t68ZxcrL2XbN6Y9/h0JvggVKx\nbTa9k75vn96ddK15dDVggHaS7b8v1yEKZ7qP1bcEdKbNsNu+XYtkBw4UEc3yqVhRxDHdOULh0al0\nOLRj6Jru/cUX2l+7/36XO6/vvCMZvcibbpJx4yQjcHrrLe1k7t+fhXa2b68BgTOvfv9+/RkWKqTZ\nI9s+dkZTf/+tqSKdOoV23qQkfd306Tpnfb167s/b7Xrr+Zln3PfbnKNBF16oEY7rvLPVqvm/422N\nRGzfrtc8NjZzvl2rXsFlhOfDnisFEFn/W+YXx5oZNkvXTyRzyqgVK4Ie6jqD6++/i3TrkCKFkCbG\nOKSIOS+f1cqcDcBu11ErtwCvb9/AKTsOh8gDD+hIjL/1EkaM0Pa+/rrP63n2rA7a9OqlcccLzztH\ns4YNczsuLU2fL1rUIZXKnZPU54drPYcx7jNVCYMBbtzy8xb1BkTsgzIYIApJvXqZi2zNn6//Svz1\nV9bP8+672p9LSMjc53DoHUjPPiKgad0HD7qfw2bT/ubTTzt3eOQqdeumN5RzzQTnaMTixRm1CRu7\njNDcDw/W80uXuu+fNUs75k2biqxdK9pDbNNGBJD4PlOlUKHMWSZPn9a61lCLtUVEc98/+yzj4QMP\n6IQ0b7yhdbnGOKRLzCxZ32G4+Jx/1WnjRl1DzW020Suv1MLT4sX1hJ4efVQ7+K531MeM0c7k77/r\n4+RkTdVZsEDXL/Dn3Dmt06hdW9vpWghrt7ulQiUliZQvdlJ6lHYfnXGrV/Hh4EE/N//tdh1qCqEG\nwnMGV3nzTTlU+EqZMCZZVr/4vX52ly/yG29oQJ0RO95+e/CALClJ05muu857+tcZM/T6BBgOGztW\nv3O7d+tHql5dxNGxk9cvy7x5eqoZJf5PYmCTd4s/o79Un3ziNRUpgwFu3PLvFvUGROyDMhggCsmD\nD2YujuqaYZJVJ09qH9JKM09K0nMDmsI+ZYrIN99owPHrr3rX/ZZbXApAJTPNYvVq3+9hs+XyFPF2\nu04JesUVknr4pJQsKTKqwkSfncZ779W+m69r9dtv+hygU/XvXXNYTjTrIJUqpkmTJu6feehQTZMP\nuDiaH1ZtqjWpUGqqyOTJItcUOyiAyGPFp0nSSe8L9tNPmnZeqpR2XDMWQ27TRp8AfHfk587V56w7\n2OvWiRQuLMlDhoVyk93bwIF6Pl/rLlgjB+vWyWuvOqQIUmV3v9Fehz3yiPbrPcsS3n5bX54x45Cn\n117Tu/FBLnzLltqfz1C/fuaq2idPam3Cm29mPH3kiA7cZARZNWqENrPWX39pZPPww5lfqmXL9Pw9\ne/r9pUxK0mDbmjnW+hH9+cw0/YV0+bI93C1FasT+I46Wt8lD7Y5K+fIOv0tPMBjgxi3/blFvQMQ+\nKIMBopCMGqVTtDscWuvZu3f2z9Wrl9Y3btqkN9NLlvSfCr1ihd7NzBgFEO3QXH11dBaJzbB3r/aS\ne/aUB7qmycU4IVuGu68qvH+/3i1+7z3/p0lP14552bKafVS7tt4Id61ltc7lUrscMptNs08aNPDu\nCKf/b7RMwgApUfi8VK7sXos7YYKmvXTsqKniVapo6r7dLjoqAGjqiC8pKdphHTNG72DXrCnba3WU\n2tfbveqBQ3LkiF5EX6lE6ekiVapI4t0PywUlbPI0xvvMgbdKGRYuzNxn5c83b65/uvTVM+3dq3f1\nAyxWduSI+yLPsm2bntB1Nbt77vGan7dLF+cETnaHdshDzbv7zJmSNmWKpk+VLq3RSICpaMeM0eDD\n+l6lpmrG1mt99otrZH0uxSGlCp2V4cXfEDl8WHbu1O+wv9pyBgPcuOXfLeoNiNgHZTBAFJI5c/Rf\nhj/+0D+zuxiqSGbHLDZWO7/btgU+3kqnnzVLb2BeeqnvhVojzlnse6rvUKmDDVKxbJrbjEvDhukN\n9FDu5p85oxMRXXqp34wd6dFD725nZdTjo4/02sXF+Xjy779FypSRfxfvkhYt9Lh+/bQeA9C0f6v/\n7baA73vv6YNAsw916qRDSU88IbMLd5cLS9qkenVdm6FECX3rsJkyRUbjOSkamyZHClX0uYKyw6Ed\n7y5d9PGYMfoRXnhBn3vhBckohfDStq1O92o5dUo74j16iOzenTHF7LFjzueHDdNhHGvRC5HMYmuX\nL3tGScLcE/qX774L/TP36aPR41VXaUTttXBBpjNn9HvlulaciNas1LnBroGIMxL6/olFWkoycZHb\nW5Up4/t7zGCAG7f8u0W9ARH7oAwGiEJirSf10EN6ozSj45MNDofWt/br57Pf5vP4rl21Y22l669f\nn/33DxuHQzu9gBy6oKpcc41DatTQa3P+vNY1PP54+N5u/Xr97KEuGnvypHbifK0e7clu17z3kiW1\nYzt5svcxjzyifdyE+Zv0LxlTE/kwbZqkoZAMxpsCiNx3n+bunz2bmfaenYWKfUk7e14uj02Q3pji\nsQqcu7fe0rvjQ4fqdRw2LHN0yeHQUofYWE2hcfPNN/qCDz/U3PlixfSX4KKLRGrVkttuTZM2bSTz\nRFWqeA+dpaToSNIrr2Tsstt1hOvhu46JleoUsnPndIrVsmUD11yIjuoVKeL947Lik52NHtCRi3//\nle6FvpEbLnE/cN8+ff2IEd7nZjDAjVv+3aLegIh9UAYDRCFJT9cOQeHCmnISaWfO6A1Qa/KZqKYI\nuTpyRKdouf122blT+2YNG+qNY8D/GlLZ1bKlLt5sff7UVK3JnTBB03xcO9iDBmnn3rMAO5B9+9zX\nP3OVmKifr3NnCfoDOLf/mDTHUilk0uWdtx1uh//5p44O9OoVersCmTlTr/Um1JaMVed8OHYsc1kB\nX3W26enaJy5WTKfR3bNHr8fBXalyuHQtXaTu+uu1EvfAAZHt2+XIxdUlBjb56D1nzr01f+6SJd5v\n4GPxi1GjRIoVtskJXOxSfRyipCSRo0cDHnL6tGYR+QpKz57Vzzq21TyRyy6Ts01aSwmTLKOGey8e\nMnCgxj6eAxAMBrhxy79b1BsQsQ/KYIAoZNdfLxnzpUfDX3/p6IC/WWGiZuvWjDnd16/XXGxjMqfH\nDydr6tQnntAa5mLFJCPlCtC6gsaNtRa1UCGR0d61tDny9dcSUkbLo4+KFCtql2XzfN/+d2ZYybRp\nOW9To0Yit7WwaSV6kPlkx4/XZRX8OXdOMlKmPLd7bjsptnT3IOjDZ3ZILNLlWOd+2skfOFCna/JV\n32DlBblUvh86JFIoxiaTijydKxHuoEH6HfE3iNOpk0ijmidFAPkaXQXwuTyBHDqk2USea+oxGODG\nLf9uUW9AxD4ogwGikHXtKj6nyYykEye8C2Hzml9/1YDAX+5/Ttjtmh1SqpSmWr35pgYg6el6x/39\n93WtrAoVNB0nKytEh8LhELn7bj2/v+yUjz+WkDr6jzyiHcwtW7Lfnvh4fS+v1J4cSE3VmZ4WLtRp\nNn/+OXPq0AED3PvsrVqJtK59SDKmJCpb1r3a3ZXNpnPlWrMGbdkiMmSI3FtkrlxTZL/fxYeza9ky\nDUp9FkY7ff65Nn1/0SpyT/UtfmvCRTRN6K233PcxGODGLf9uUW9AxD4ogwGikI0cqakCASYtIafc\nnNo0LS34+R2O3EulOnBA0+LLlPFej2vtWp2GtF+/4OdJTtbRpnLl3Gcyyopu3TTzJhIB4uTJ4lY3\nffSoziI0ebJo6pA1hBDo/5NBgzRvp359PbZMGdnec4SULGGXBx8M388sOVmvy803+1/PTUTrSgoX\nFhnx7CkpWtQRsCbcFwYD3Ljl3y0GREQeBg0C1q0DihSJdkvyvkKFcu/chQsHP78xuuWGyy8HVq8G\nrr0WuO024IsvdH9iInDffcANNwATJgQ/T4kSwOLFQO3aQJs2wKuvAnZ76O3Ytw+YPRt46ikgJgL/\na/XtCzz/PPDMM8CsWcB33+k1vuce6M4hQ4CWLYG6df2f5JFHgPR04IorgDlzgIMHUX36S5j8UQy+\n+AKYPj08bX3pJeDAAeCTT4DYWP/HXXwx0KoVMGLiRTh/3qBr1/C8PxH99+Xif2NE9F9VogRQpUq0\nW0F5waWXAosWAf37Aw89BGzbBvzxB3D2LLBsGVC0aGjnKVcOmD8fGDVKg4Fly4AZM4Dy5fV5EeDU\nKcBmAy67zP21770HXHAB0KtXOD9ZYCNHAnv36me+6iqgRQurXQYYPz74CW64AThzxmt3jx7Ar78C\nAwYAN92kgZar/fuBefOA3r2DB4IrVmgwNm4cUKNG8CZ17qw/g1tuAa68MvjxRFQwcGSAiIgCKlIE\nmDoVeOMN7cwvWgR89RVQqVLWzhMbCwwbBixZAmzdqv3lxo2BypWB4sWB0qWBsmX1DvaMGUBqKpCc\nDHz0EdCnjwYEkRITA0ybBjRqBPzzD9ClS/jOPXGifuZu3YCUFN137hzwv/9pp75fPx2ZCCQlRQOG\nxo11JC8UHTtq8NajR46aT0T5DEcGiIgoKGOAZ58F6tTRG96tW2f/XC1aABs3Aq+8opk0t90GVKig\nowRJScCnn2qH9ZJLgBtv1Pd74olwfZLQFS2qGT7vvgs88ED4zluyJPDNN0DDhpr61LatZh8lJACD\nBwOlSgEvv6yBiL8g5KWXNH1q7tzA6UGuLrtMA5srrgjfZyGi/z4jItFuQ0QYY+oBWLdu3TrUq1cv\n2s0hIqIAtm/XPPjp04Hbbwc++yzaLQq/qVOBRx/Vv7dvD7z1FlCtmqZM3X8/8NNPwJo17qlENpuW\nLEycqMcPHhyZtq5fvx7169cHgPoisj4y70pEkcBggIiI8iyR3CuQjjYRrYeoWhW44w73586e1RSg\n9HRg7VodLUhMBLp21XqLCROAxx6L3LVhMECUfzFNiIiI8qz8GggA+tn8pT9dcIGmKDVooIXTr74K\ndOqkaVSLFmmqFRFROLCAmIiIKA+qVg34/HMNCurV09GBtWsZCBBReHFkgIiIKI/q0EFrA7ZuBd5+\nW4uPiYjCicEAERFRHhapImEiKpiYJkREREREVEAxGCAiIiIiKqAYDBARERERFVAMBoiIiIiICigG\nA0REREREBRSDASIiIiKiAorBABERERFRAcVggIiIiIiogGIwQERERERUQDEYICIiIiIqoBgMEBER\nEREVUAwGiIiIiIgKKAYDREREREQFVJ4JBowxA4wxu40x54wxq4wxDYMc38IYs84Yk2qM+ccY83Ck\n2kqhmzlzZrSbUODwmkcer3nk8ZoTEYVHnggGjDHdALwJYDiAugA2AVhgjCnj5/jKAH4CsARAHQAT\nAEwxxrSJRHspdPwPO/J4zSOP1zzyeM2JiMIjTwQDAAYDmCwin4nINgD9AaQA6O3n+McA7BKRZ0Vk\nu4i8B2C28zxERERERBSCqAcDxpjCAOpD7/IDAEREACwG0MTPyxo7n3e1IMDxRERERETkIerBAIAy\nAGIBHPHYfwRAeT+vKe/n+FLGmKLhbR4RERERUf5UKNoNiKBiALB169Zot6NAOX36NNavXx/tZhQo\nvOaRx2seebzmkeXyf2exaLaDiMIvLwQDxwHYAZTz2F8OwGE/rzns5/gzInLez2sqA8CDDz6YvVZS\nttWvXz/aTShweM0jj9c88njNo6IygLhoN4KIwifqwYCIpBtj1gFoBeBHADDGGOfjiX5eFg+gnce+\n2537/VkAoAeAPQBSc9BkIiKigqYYNBBYEOV2EFGYGa3VjXIjjOkK4FPoLEJroLMC3QegpogcM8aM\nBlBRRB52Hl8ZwBYA7wP4BBo4vAPgThHxLCwmIiIiIiIfoj4yAAAi8o1zTYHXoek+GwG0FZFjzkPK\nA7jS5fg9xpi7ALwNYCCAAwD+j4EAEREREVHo8sTIABERERERRV5emFqUiIiIiIiigMEAEREREVEB\nVSCCAWPMAGPMbmPMOWPMKmNMw2i3Kb8wxrxgjFljjDljjDlijJljjKnu47jXjTEJxpgUY8wiY0zV\naLQ3vzHGPG+McRhj3vLYz+sdZsaYisaYz40xx53XdZMxpp7HMbzuYWKMiTHG/M8Ys8t5PXcaY172\ncRyveTYZY5oZY340xhx0/jvSwccxAa+vMaaoMeY95+9FkjFmtjGmbOQ+BRHlVL4PBowx3QC8CWA4\ngLoANgFY4CxYppxrBmASgEYAWgMoDGChMaa4dYAx5jkATwDoC+AmAMnQn0GRyDc3/3AGtX2h32nX\n/bzeYWaMuRjASgDnAbQFUAvAEAAnXY7hdQ+v5wH0A/A4gJoAngXwrDHmCesAXvMcKwmdsONxAF4F\nhCFe33cA3AWgM4BbAVQE8G3uNpuIwinfFxAbY1YBWC0iTzkfGwD7AUwUkbFRbVw+5AyyjgK4VURW\nOPclABgnIm87H5cCcATAwyLyTdQa+x9mjLkAwDoAjwEYBmCDiDztfI7XO8yMMWMANBGR5gGO4XUP\nI2PMXACHRaSPy77ZAFJEpKfzMa95mBhjHAA6iciPLvsCXl/n42MAuovIHOcxNQBsBdBYRNZE+nMQ\nUdbl65EBY0xhAPUBLLH2iUY/iwE0iVa78rmLoXeYTgCAMeZq6NSwrj+DMwBWgz+DnHgPwFwR+dV1\nJ693rrkbwB/GmG+c6XDrjTGPWk/yuueKOACtjDHVAMAYUwdAUwC/OB/zmueiEK9vA+gU5a7HbAew\nD/wZEP1n5Il1BnJRGQCx0DsZro4AqBH55uRvzlGXdwCsEJG/nbvLQ4MDXz+D8hFsXr5hjOkO4Ebo\nf8SeeL1zxzXQUZg3AYyEpkxMNMacF5HPweueG8YAKAVgmzHGDr159ZKIfOV8ntc8d4VyfcsBSHMG\nCf6OIaI8Lr8HAxRZ7wO4Fnr3jnKBMeYKaMDVWkTSo92eAiQGwBoRGeZ8vMkYcz101fTPo9esfK0b\ngAcAdAfwNzQAnmCMSXAGYEREFAb5Ok0IwHEAdujdC1flAByOfHPyL2PMuwDuBNBCRA65PHUYgAF/\nBuFSH8BlANYbY9KNMekAmgN4yhiTBr0jx+sdfoegedCutgKo5Pw7v+fhNxbAGBGZJSJ/iciX0FXn\nX3A+z2ueu0K5vocBFHHWDvg7hojyuHwdDDjvnK4D0Mra50xlaQXNR6UwcAYCHQG0FJF9rs+JyG7o\nfwquP4NS0NmH+DPIusUAakPvktZxbn8A+AJAHRHZBV7v3LAS3qmFNQDsBfg9zyUloDfwHSJ7AAAF\nkElEQVRzXDng/H+L1zx3hXh91wGweRxTAxokx0essUSUIwUhTegtAJ8aY9YBWANgMPQ/mU+j2aj8\nwhjzPoD7AXQAkGyMse4inRaRVOff3wHwsjFmJ4A9AP4H4ACAHyLc3P88EUmGpkxkMMYkA0gUEevO\nNa93+L0NYKUx5gUA30A7RI8C6ONyDK97eM2FXs8DAP4CUA/67/cUl2N4zXPAGFMSQFXoCAAAXOMs\n1D4hIvsR5PqKyBljzFQAbxljTgJIAjARwErOJET035HvgwHn9GdlALwOHbrcCKCtiByLbsvyjf7Q\nIrOlHvsfAfAZAIjIWGNMCQCTobMNLQfQTkTSItjO/MxtfmBe7/ATkT+MMfdAi1qHAdgN4CmXYlZe\n9/B7Atr5fA9AWQAJAD5w7gPAax4GDQD8Bv03RKAF8gAwHUDvEK/vYOgIzmwARQHMBzAgMs0nonDI\n9+sMEBERERGRb/m6ZoCIiIiIiPxjMEBEREREVEAxGCAiIiIiKqAYDBARERERFVAMBoiIiIiICigG\nA0REREREBRSDASIiIiKiAorBABERERFRAcVggIjyJGPMw8aYk9FuBxERUX7GYICIAjLGTDPGOFy2\n48aYecaY2lk4x3BjzIZsvD2XSCciIspFDAaIKBTzAJQDUB7AbQBsAOZm8Rzs2BMREeUxDAaIKBTn\nReSYiBwVkc0AxgC40hhzKQAYY8YYY7YbY5KNMf8aY143xsQ6n3sYwHAAdZwjC3ZjTE/ncxcZYyYb\nYw4bY84ZYzYbY+50fWNjzO3GmL+NMUnOEYlyHs8/6nz+nPPPx1yeK2yMedcYk+B8frcx5rncvVRE\nRET/HYWi3QAi+m8xxlwA4CEAO0Qk0bn7DICeAA4BqA3gY+e+8QC+BnA9gLYAWgEwAE4bYwyA+QBK\nAngAwC4ANTzeriSAIQB6QEcWvnSe8yFnW3oAeBXAAAAbAdQF8LEx5qyIfA7gKQDtAdwHYD+AK50b\nERERgcEAEYXmbmNMkvPvJQEkQDvZAAARGeVy7D5jzJsAugEYLyKpxpizAGwicsw6yBhzO4AGAGqK\nyL/O3Xs83rcQgH4issf5mncBDHN5/lUAQ0TkB+fjvcaY6wD0A/A5tOO/Q0TinM/vz+oHJyIiys8Y\nDBBRKH4F0B96V/8SAI8DmG+MaSgi+40x3QA8CaAKgAug/7acDnLOOgAOuAQCvqRYgYDTIQBlAcAY\nU8L5flONMVNcjokFcMr5908BLDLGbIeOQvwkIouCtIuIiKjAYDBARKFIFpHd1gNjTB9oZ7+PMeYX\nAF9A79gvdO6/H8DTQc55LoT3Tfd4LNCABNCgAwAeBbDG4zg7AIjIBmNMZQDtALQG8I0xZpGIdA3h\nvYmIiPI9BgNElF0CoDiAmwHsEZEx1hPODrirNOgde1ebAVxhjKkqIjuz/OYiR40xCQCqiMhXAY47\nC2AWgFnGmG8BzDPGXCwip/y9hoiIqKBgMEBEoSjqMovPJdCUoBLQ6UUvAlDJmSq0FlpL0Mnj9XsA\nXG2MqQPgAIAkEVlmjFkO4FtjzBAAOwHUBOAQkYUhtms4gAnGmDPQNKCi0DqEi0XkHWPMYGhq0QZo\n8NIVwGEGAkRERIpTixJRKO6AFg0nAFgFoD6A+0RkmYjMBfA2gEnQTndjAK97vP5baGf9NwBHAXR3\n7r8XGkDMAPAXgDfgPYLgl4hMhaYJPQIdaVgK4GEAVkpTEoBnne+xGkAlAHd6nYiIiKiAMiJcB4iI\niIiIqCDiyAARERERUQHFYICIiIiIqIBiMEBEREREVEAxGCAiIiIiKqAYDBARERERFVAMBoiIiIiI\nCigGA0REREREBRSDASIiIiKiAorBABERERFRAcVggIiIiIiogGIwQERERERUQDEYICIiIiIqoP4f\n6JjmOGnFUeIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc04a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   96.920% -- Uniform [-0.1, 0.1)\n",
      "   97.200% -- Normal stddev 0.1\n",
      "Loss\n",
      "    0.103  -- Uniform [-0.1, 0.1)\n",
      "    0.099  -- Normal stddev 0.1\n"
     ]
    }
   ],
   "source": [
    "normal_01_weights = [\n",
    "    tf.Variable(tf.random_normal(layer_1_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.random_normal(layer_2_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.random_normal(layer_3_weight_shape, stddev=0.1))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Uniform [-0.1, 0.1) vs Normal stddev 0.1',\n",
    "    [\n",
    "        (uniform_neg01to01_weights, 'Uniform [-0.1, 0.1)'),\n",
    "        (normal_01_weights, 'Normal stddev 0.1')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal distribution gave a slight increasse in accuracy and loss.  Let's move closer to 0 and drop picked numbers that are `x` number of standard deviations away.  This distribution is called [Truncated Normal Distribution](https://en.wikipedia.org/wiki/Truncated_normal_distribution%29).\n",
    "### Truncated Normal Distribution\n",
    ">[tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)](https://www.tensorflow.org/api_docs/python/tf/truncated_normal)\n",
    "\n",
    ">Outputs random values from a truncated normal distribution.\n",
    "\n",
    ">The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.\n",
    "\n",
    ">- **shape:** A 1-D integer Tensor or Python array. The shape of the output tensor.\n",
    "- **mean:** A 0-D Tensor or Python value of type dtype. The mean of the truncated normal distribution.\n",
    "- **stddev:** A 0-D Tensor or Python value of type dtype. The standard deviation of the truncated normal distribution.\n",
    "- **dtype:** The type of the output.\n",
    "- **seed:** A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.\n",
    "- **name:** A name for the operation (optional)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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APyX2ixuIfetbyfY+gNhnthL7V9NavQcghHCrpG8CH04eM72HuN8cmrQ5bRUx2DyUeFMc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\nYGZmlkMOAMzMzHLIAYCZmVkO/X+vp+lxH+vC3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d35f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helper.hist_dist('Truncated Normal (mean=0.0, stddev=1.0)', tf.truncated_normal([1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's compare the previous results with the previous distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y7bffMmPGDFq0aJEuYa1cuTKNGzfmueeeo0GDBlSpUoVWrVrRrFkzpk+fnnYj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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1221f03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   97.020% -- Normal\n",
      "   97.480% -- Truncated Normal\n",
      "Loss\n",
      "    0.088  -- Normal\n",
      "    0.034  -- Truncated Normal\n"
     ]
    }
   ],
   "source": [
    "trunc_normal_01_weights = [\n",
    "    tf.Variable(tf.truncated_normal(layer_1_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.truncated_normal(layer_2_weight_shape, stddev=0.1)),\n",
    "    tf.Variable(tf.truncated_normal(layer_3_weight_shape, stddev=0.1))\n",
    "]\n",
    "\n",
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Normal vs Truncated Normal',\n",
    "    [\n",
    "        (normal_01_weights, 'Normal'),\n",
    "        (trunc_normal_01_weights, 'Truncated Normal')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's no difference between the two, but that's because the neural network we're using is too small. A larger neural network will pick more points on the normal distribution, increasing the likelihood it's choices are larger than 2 standard deviations.\n",
    "\n",
    "We've come a long way from the first set of weights we tested. Let's see the difference between the weights we used then and now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M7LSsrCx30kknuRNOOCE77eqrr3bJyclu+fLl2WkbN2501atXz3WP0dx2220u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\n31hdBlweU0ZtXrbG4O25CPgaaIf//n48pozavBTMrCFwEN4jAXBAJCF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f4d2710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After 858 Batches (2 Epochs):\n",
      "Validation Accuracy\n",
      "   66.100% -- Baseline\n",
      "   97.040% -- Truncated Normal\n",
      "Loss\n",
      "   24.090  -- Baseline\n",
      "    0.075  -- Truncated Normal\n"
     ]
    }
   ],
   "source": [
    "helper.compare_init_weights(\n",
    "    mnist,\n",
    "    'Baseline vs Truncated Normal',\n",
    "    [\n",
    "        (basline_weights, 'Baseline'),\n",
    "        (trunc_normal_01_weights, 'Truncated Normal')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a huge difference. You can barely see the truncated normal line.  However, this is not the end your learning path.  We've provided more resources for initializing weights in the classroom!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
